{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56210123",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51c3faaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "\n",
    "#Plots\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "#Machine Learning Libraries\n",
    "import jax\n",
    "from jax import jit, grad, vmap, value_and_grad\n",
    "from jax.config import config\n",
    "import jax.numpy as jnp\n",
    "import optax\n",
    "import jaxopt\n",
    "from typing import List, Dict, Tuple\n",
    "from pyDOE import lhs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6283d3e",
   "metadata": {},
   "source": [
    "# Auxiliary Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "063281c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Encode_Fourier(X,M,N):\n",
    "    x=X[0]\n",
    "    y=X[1]\n",
    "    P_x=2\n",
    "    P_y=2\n",
    "    n_num = jnp.arange(1, N+1)\n",
    "    m_num = jnp.arange(1, M+1)\n",
    "    n, m = jnp.meshgrid(n_num, m_num)\n",
    "    n=n.flatten()\n",
    "    m=m.flatten()\n",
    "    w_x = 2.0 * jnp.pi / P_x\n",
    "    w_y = 2.0 * jnp.pi / P_y    \n",
    "    out = jnp.hstack([jnp.cos(n* w_x * x)  * jnp.cos(m * w_y * y),\n",
    "                      jnp.cos(n * w_x * x) * jnp.sin(m * w_y * y),\n",
    "                      jnp.sin(n * w_x * x) * jnp.cos(m * w_y * y),])\n",
    "    return out\n",
    "def identity(X,X_min,X_max):\n",
    "    return X\n",
    "def MSE(pred,exact,weight=1):\n",
    "    return jnp.mean(weight*jnp.square(pred - exact))\n",
    "def relative_error2(pred,exact):\n",
    "    return np.linalg.norm(exact-pred,2)/np.linalg.norm(exact,2)\n",
    "#Initialization\n",
    "def glorot_normal(in_dim, out_dim):\n",
    "    glorot_stddev = np.sqrt(2.0 / (in_dim + out_dim))\n",
    "    W = glorot_stddev * jnp.array(np.random.normal(size=(in_dim, out_dim)))\n",
    "    return W\n",
    "def init_params(layers, Mod_MLP=True):\n",
    "    params = []\n",
    "    in_dim, out_dim = layers[0], layers[1]\n",
    "    U1=glorot_normal(in_dim, out_dim)\n",
    "    b1=jnp.zeros(out_dim)\n",
    "    U2=glorot_normal(in_dim, out_dim)\n",
    "    b2=jnp.zeros(out_dim)\n",
    "    for i in range(len(layers) - 1):\n",
    "        in_dim, out_dim = layers[i], layers[i + 1]\n",
    "        W = glorot_normal(in_dim, out_dim)\n",
    "        b = jnp.zeros(out_dim)\n",
    "        if i ==0:\n",
    "            params.append({\"W\": W, \"b\": b, \"U1\":U1, \"b1\":b1,\"U2\":U2, \"b2\":b2,})\n",
    "        else:\n",
    "            params.append({\"W\": W, \"b\": b})\n",
    "    return params\n",
    "\n",
    "def FCN_MMLP(params,X_in,M1,M2,activation,norm_fn):#Modified MLP\n",
    "    inputs =norm_fn(X_in,M1,M2)\n",
    "    # MMLP\n",
    "    U1=params[0][\"U1\"]\n",
    "    U2=params[0][\"U2\"]\n",
    "    b1=params[0][\"b1\"]\n",
    "    b2=params[0][\"b2\"]\n",
    "    U =activation(jnp.dot(inputs, U1) + b1)\n",
    "    V =activation(jnp.dot(inputs, U2) + b2)\n",
    "    for layer in params[:-1]:\n",
    "        outputs = activation(inputs @ layer[\"W\"] + layer[\"b\"]) \n",
    "        inputs  = jnp.multiply(outputs, U) + jnp.multiply(1 - outputs, V) \n",
    "    W = params[-1][\"W\"]\n",
    "    b = params[-1][\"b\"]\n",
    "    outputs = jnp.dot(inputs, W) + b\n",
    "    return outputs\n",
    "def plot3D_mat(x,y,F,f_names,window=4,font_size='10',x_label='x',y_label='y',cmap='rainbow',fig_width=6):\n",
    "  X,Y= x,y\n",
    "  fig,ax=plt.subplots(1,len(F),figsize=(fig_width*len(F),window))\n",
    "  plt.rcParams['font.size'] = font_size\n",
    "  for i in range(len(ax)):\n",
    "    cp = ax[i].contourf(X,Y, F[i],50,cmap=cmap)\n",
    "    fig.colorbar(cp) # Add a colorbar to a plot\n",
    "    ax[i].set_title(f_names[i])\n",
    "    ax[i].set_xlabel(x_label)\n",
    "    if i==0:\n",
    "        ax[i].set_ylabel(y_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964d9e65",
   "metadata": {},
   "source": [
    "# Tunning Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5cec69f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Architecture:\n",
      "[2, 128, 128, 128, 128, 128, 128, 1]\n"
     ]
    }
   ],
   "source": [
    "Mode='Fourier'\n",
    "use_RBA=True\n",
    "Mod_MLP=True\n",
    "seed_np=5555\n",
    "\n",
    "#Model\n",
    "num_layer=6\n",
    "width_layer = 128 # neurons/layer\n",
    "layers = [2] + num_layer*[width_layer] + [1]\n",
    "print('Model Architecture:')\n",
    "print(layers)\n",
    "#Optimization\n",
    "num_epochs_adam =20*(10**3)\n",
    "num_epochs_lbfgsb =1*(10**3)\n",
    "substepts_lbfgs=5\n",
    "#Global weights\n",
    "lamB =100\n",
    "lamE =1\n",
    "#learning \n",
    "lr0=5*10**(-3)\n",
    "lrf=1*10**(-5)\n",
    "#RBA\n",
    "lr_lambdas_0=1*10**(-3)\n",
    "decay_rate=0.7\n",
    "decay_step=1000\n",
    "gamma=0.9999\n",
    "lam_min=0.0\n",
    "init_zero=True\n",
    "#Number of training points\n",
    "N0 = 50\n",
    "N_b = 50\n",
    "nxb, nyb = (101,101)\n",
    "N_f = nxb*nyb\n",
    "#Fourier \n",
    "N=5\n",
    "M=5\n",
    "#Helmhotz Details\n",
    "a1 = 1.0\n",
    "a2 = 4.0\n",
    "ksq = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5550626",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Model Architecture:\n",
      "[75, 128, 128, 128, 128, 128, 128, 1]\n",
      "Mode:\n",
      "Fourier\n"
     ]
    }
   ],
   "source": [
    "# Settings\n",
    "if Mode.lower()  == 'fourier':\n",
    "    dim_in = N * M * 3\n",
    "    layers = [dim_in] + num_layer*[width_layer] + [1]\n",
    "    print('New Model Architecture:')\n",
    "    print(layers)\n",
    "    print('Mode:')\n",
    "    print(Mode)\n",
    "activation_fn=jnp.tanh\n",
    "if Mode.lower()=='adf':\n",
    "    norm_fn    = identity\n",
    "    Norm_metric1=lambda x: 0\n",
    "    Norm_metric2=lambda x: 0  \n",
    "elif Mode.lower()=='fourier':\n",
    "    norm_fn    = Encode_Fourier\n",
    "    Norm_metric1=lambda x: N\n",
    "    Norm_metric2=lambda x: M  \n",
    "# Errors\n",
    "Error_fn=MSE\n",
    "# Initialize NN\n",
    "np.random.seed(seed_np)\n",
    "params = init_params(layers) \n",
    "pinn_fn = FCN_MMLP\n",
    "#Optimizer\n",
    "optimizer_w = optax.adam(optax.exponential_decay(lr0, decay_step, decay_rate,))\n",
    "opt_state_w = optimizer_w.init(params)\n",
    "M1= Norm_metric1(1)\n",
    "M2= Norm_metric2(1)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914bff94",
   "metadata": {},
   "source": [
    "## Generate Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2424041",
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = np.array([-1.0])\n",
    "ub = np.array([1.0])\n",
    "rb = np.array([1.0])\n",
    "lftb = np.array([-1.0])\n",
    "nx, ny = (1001,1001)\n",
    "x = np.linspace(-1, 1, nx)\n",
    "y = np.linspace(-1, 1, ny)\n",
    "xv, yv = np.meshgrid(x,y)\n",
    "x = np.reshape(x, (-1,1))\n",
    "y = np.reshape(y, (-1,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "428d9ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exact solution to compute errors and evaluate performance\n",
    "Exact_u = np.sin(a1*np.pi*xv)*np.sin(a2*np.pi*yv)\n",
    "X,Y=np.meshgrid(x,y,indexing='ij')\n",
    "u=np.reshape(Exact_u ,(nx,ny)).T\n",
    "Exact=np.array([u,u]) #we store them this way only to plot them\n",
    "X_exact=np.hstack((X.flatten()[:,None],\n",
    "                   Y.flatten()[:,None],\n",
    "                   u.flatten()[:,None]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cf7f23",
   "metadata": {},
   "source": [
    "# Training Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "530357df",
   "metadata": {},
   "outputs": [],
   "source": [
    "xb = np.linspace(-1, 1, N_b)\n",
    "yb = np.linspace(-1, 1, N_b)\n",
    "Xb, Yb = np.meshgrid(xb,yb)\n",
    "xb = np.reshape(xb, (-1,1))\n",
    "yb = np.reshape(yb, (-1,1))\n",
    "X_lb = np.hstack((Xb[0,:].flatten()[:,None],Yb[0,:].flatten()[:,None]))\n",
    "X_ub = np.hstack((Xb[-1,:].flatten()[:,None],Yb[-1,:].flatten()[:,None]))\n",
    "X_rb = np.hstack((Xb[:,0].flatten()[:,None],Yb[:,0].flatten()[:,None]))\n",
    "X_lftb = np.hstack((Xb[:,-1].flatten()[:,None],Yb[:,-1].flatten()[:,None]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c945d1d1",
   "metadata": {},
   "source": [
    "# Collocation Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87610543",
   "metadata": {},
   "outputs": [],
   "source": [
    "xb = np.linspace(-1, 1, nxb)\n",
    "yb = np.linspace(-1, 1, nyb)\n",
    "Xb, Yb = np.meshgrid(xb,yb)\n",
    "xb = np.reshape(xb, (-1,1))\n",
    "yb = np.reshape(yb, (-1,1))\n",
    "X_f=np.hstack((Xb.flatten()[:,None],Yb.flatten()[:,None]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc09619",
   "metadata": {},
   "source": [
    "# Define data details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24380702",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_res    =X_f\n",
    "X_BCs_U  =X_ub\n",
    "X_BCs_L  =X_lb\n",
    "X_BCs_R  =X_rb\n",
    "X_BCs_Lf  =X_lftb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28ce12b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize = (6,6)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "plt.rcParams['font.size'] = '16'\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Scatter plots for each boundary condition or region with labels for the legend\n",
    "ax.scatter(X_BCs_L[:,0], X_BCs_L[:,1], c='b', s=5, label='Left Boundary')\n",
    "ax.scatter(X_BCs_U[:,0], X_BCs_U[:,1], c='r', s=5, label='Upper Boundary')\n",
    "ax.scatter(X_BCs_R[:,0], X_BCs_R[:,1], c='yellow', s=5, label='Right Boundary')\n",
    "ax.scatter(X_BCs_Lf[:,0], X_BCs_Lf[:,1], c='k', s=5, label='Lower Boundary')\n",
    "ax.scatter(X_res[:,0], X_res[:,1], c='g', s=0.05, label='Residual Points')\n",
    "\n",
    "# Adding a legend to the plot to differentiate the scatter points\n",
    "plt.legend()\n",
    "\n",
    "# Adding a title and axis labels\n",
    "plt.title('Domain Visualization')\n",
    "plt.xlabel('X Axis Label')\n",
    "plt.ylabel('Y Axis Label')\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354aae62",
   "metadata": {},
   "source": [
    "# Initilize RBA Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f41147c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting Zero Initialization\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(seed_np)\n",
    "lambda_bcs=np.random.uniform(0,1,X_BCs_U.shape[0]*layers[-1]).reshape(X_BCs_U.shape[0],layers[-1])\n",
    "lambda_res=np.random.uniform(0,1,X_res.shape[0]*layers[-1]).reshape(X_res.shape[0],layers[-1])\n",
    "if init_zero:\n",
    "    print('Setting Zero Initialization')\n",
    "    lambda_bcs=lambda_bcs\n",
    "    lambda_res=lambda_res*0\n",
    "lambdas={\n",
    "         'BCs':lambda_bcs,\n",
    "         'Res':lambda_res}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49bd73e3",
   "metadata": {},
   "source": [
    "# Loss Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1416b8f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Choosen Model: <function u_PINN at 0x7f256c491430>\n",
      "Weights for BCs and eq:100,1\n"
     ]
    }
   ],
   "source": [
    "def u_PINN(params):\n",
    "    u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "    u = lambda x: u_NN(x)[0]\n",
    "    return u    \n",
    "def u_adf(params):\n",
    "    u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "    u    = lambda x: (x[0]**2-1)*(x[1]**2-1)*u_NN(x)[0]   \n",
    "    return u   \n",
    "if Mode.lower()=='adf':\n",
    "    u_model=u_adf\n",
    "    lamB=0\n",
    "    #Def PDE\n",
    "    def PDE(params):\n",
    "        u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "        u    = lambda x: (x[0]**2-1)*(x[1]**2-1)*u_NN(x)[0]  \n",
    "        # derivatives\n",
    "        u_x              = lambda x: grad(u)(x)[0]\n",
    "        u_y              = lambda x: grad(u)(x)[1]\n",
    "        u_xx             = lambda x: grad(u_x)(x)[0]\n",
    "        u_yy             = lambda x: grad(u_y)(x)[1]\n",
    "        #Forcing Term\n",
    "        q                = lambda x:(- (a1*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) - \n",
    "                                    (a2*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) + \n",
    "                                        ksq*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]))\n",
    "        # equations\n",
    "        eq1 = (\n",
    "                        lambda x:  u_xx(x) + u_yy(x) + ksq*u(x) - q(x)\n",
    "        )\n",
    "        \n",
    "        return eq1\n",
    "else:\n",
    "    u_model=u_PINN\n",
    "    #Def PDE\n",
    "    def PDE(params):\n",
    "        u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "        u = lambda x: u_NN(x)[0]\n",
    "        # derivatives\n",
    "        u_x              = lambda x: grad(u)(x)[0]\n",
    "        u_y              = lambda x: grad(u)(x)[1]\n",
    "        u_xx             = lambda x: grad(u_x)(x)[0]\n",
    "        u_yy             = lambda x: grad(u_y)(x)[1]\n",
    "        #Forcing Term\n",
    "        q                = lambda x:(- (a1*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) - \n",
    "                                    (a2*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) + \n",
    "                                        ksq*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]))\n",
    "        # equations\n",
    "        eq1 = (\n",
    "                        lambda x:  u_xx(x) + u_yy(x) + ksq*u(x) - q(x)\n",
    "        )\n",
    "        \n",
    "        return eq1\n",
    "print('Choosen Model:',u_model)\n",
    "print(f'Weights for BCs and eq:{lamB},{lamE}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75dadc90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Adam Optimizer\n",
    "@jit\n",
    "def update(params, \n",
    "           lambdas,\n",
    "           opt_state_w, \n",
    "           data: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray],\n",
    "           idx_res,\n",
    "           epoch):\n",
    "    X_BCs_U, X_BCs_L,X_BCs_R,X_BCs_Lf, X_res  = data\n",
    "    X_res_subset=X_res[idx_res]\n",
    "    eq1                 = PDE(params)\n",
    "    #compute Errors\n",
    "    e_helmholtz         = vmap(eq1, (0))(X_res_subset)[:,None]\n",
    "    r_j=jnp.abs(e_helmholtz)\n",
    "    #Update lambdas\n",
    "    lam_res             = (gamma)*lambdas['Res'][idx_res] + lr_lambdas_0*(r_j/jnp.max(r_j))\n",
    "    lambdas['Res']      = lambdas['Res'].at[idx_res].set(lam_res)\n",
    "    def loss_fn(params):\n",
    "        X_res_subset=X_res[idx_res]\n",
    "        # Call Functions\n",
    "        eq1                 = PDE(params)\n",
    "        u_fx                = u_model(params)\n",
    "        # Residuals prediction\n",
    "        e_helmholtz         = vmap(eq1, (0))(X_res_subset)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_up          = X_BCs_U[:, :2]\n",
    "        u_pred_bcs_up  = vmap(u_fx, (0))(tx_up)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_low        = X_BCs_L[:, :2]\n",
    "        u_pred_bcs_low= vmap(u_fx, (0))(tx_low)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_r        = X_BCs_R[:, :2]\n",
    "        u_pred_bcs_r= vmap(u_fx, (0))(tx_r)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_lft        = X_BCs_Lf[:, :2]\n",
    "        u_pred_bcs_lft= vmap(u_fx, (0))(tx_lft)[:,None]\n",
    "        #Add \n",
    "        # Loss Weights\n",
    "        lamE_it=use_RBA*lambdas['Res'][idx_res]+(1-use_RBA)+lam_min    \n",
    "        lamB_it=1\n",
    "        #Loss Equation\n",
    "        loss_helmholtz           = lamE*Error_fn(e_helmholtz,0.0,weight=lamE_it)\n",
    "        #Loss Bcs u\n",
    "        loss_up                  = lamB*Error_fn(u_pred_bcs_up,0.0,weight=lamB_it)\n",
    "        loss_low                 = lamB*Error_fn(u_pred_bcs_low,0.0,weight=lamB_it)\n",
    "        loss_r                   = lamB*Error_fn(u_pred_bcs_r,0.0,weight=lamB_it)\n",
    "        loss_lf                  = lamB*Error_fn(u_pred_bcs_lft,0.0,weight=lamB_it)\n",
    "        #Total Loss\n",
    "        loss                = (        \n",
    "                              + loss_helmholtz\n",
    "                              + loss_up\n",
    "                              + loss_low\n",
    "                              + loss_r\n",
    "                              + loss_lf\n",
    "                              )\n",
    "        return loss, (\n",
    "                     (loss,\n",
    "                      Error_fn(e_helmholtz,0.0),\n",
    "                      jnp.mean(jnp.square(u_pred_bcs_up)\n",
    "                     +jnp.square(u_pred_bcs_low)\n",
    "                     +jnp.square(u_pred_bcs_up)\n",
    "                     +jnp.square(u_pred_bcs_up)),),\n",
    "                     lambdas\n",
    "        )\n",
    "\n",
    "    grad_fn = value_and_grad(loss_fn, has_aux=True)\n",
    "    #Update weights\n",
    "    (_, aux_vals),all_grads=grad_fn(params)\n",
    "    losses,lambdas         =aux_vals\n",
    "    grad_w                 =all_grads\n",
    "    updates, opt_state_w = optimizer_w.update(grad_w, opt_state_w, params)\n",
    "    params  = optax.apply_updates(params, updates)\n",
    "    return params, lambdas, opt_state_w, losses, grad_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e569fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_errors(epoch,Errors,N_its,params,X_exact):\n",
    "    N_its=int(N_its/100)\n",
    "    errors=np.zeros((1,3))\n",
    "    eq1                 = PDE(params)\n",
    "    u_fx                = u_model(params)\n",
    "    # Residuals prediction\n",
    "    e_helmholtz   = vmap(eq1, (0))(X_res)[:,None]\n",
    "    # Prediction \n",
    "    xy, u_real          = X_exact[:, :2], X_exact[:, 2:]\n",
    "    u_pred              = vmap(u_fx, (0))(xy)[:,None]\n",
    "    u_pred              =jax.device_get(u_pred)\n",
    "    #Compute errors\n",
    "    errors[0,0]=relative_error2(u_pred,u_real)\n",
    "    errors[0,1]=np.linalg.norm(u_real-u_pred,np.inf)\n",
    "    errors[0,2]=Error_fn(e_helmholtz,0.0)\n",
    "    Errors=np.vstack((Errors,errors))\n",
    "    n_errors=np.arange(len(Errors)-1)*100\n",
    "    return u_pred,e_helmholtz,errors,Errors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fb2367",
   "metadata": {},
   "source": [
    "# ADAM Training \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c57cd274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Epoch: 1, It: 1, Time: 27.78s, Learning Rate: 5.0e-03\n",
      "Epoch: 1, It: 1, Total Error: 7.053e+04, Total Loss: 2.318e+02\n",
      "Epoch: 1, It: 1, Relative L2: 1.555e+00, Relative Linfty: 2.547e+00\n",
      "Epoch: 1, It: 1, Loss_helmholtz: 7.053e+04, Loss BCs(u): 3.240e+00\n",
      "Epoch: 1, It: 1, Lambda BCs: 6.377e+01, Lambda PDE: 1.478e-04\n",
      "--------------------------------------------------\n",
      "Epoch: 50, It: 50, Time: 1.22s, Learning Rate: 4.9e-03\n",
      "Epoch: 50, It: 50, Total Error: 3.538e+04, Total Loss: 1.174e+02\n",
      "Epoch: 50, It: 50, Relative L2: 2.121e-01, Relative Linfty: 3.070e-01\n",
      "Epoch: 50, It: 50, Loss_helmholtz: 3.538e+04, Loss BCs(u): 1.621e+00\n",
      "Epoch: 50, It: 50, Lambda BCs: 6.377e+01, Lambda PDE: 1.125e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 100, It: 100, Time: 1.24s, Learning Rate: 4.8e-03\n",
      "Epoch: 100, It: 100, Total Error: 2.359e+04, Total Loss: 7.832e+01\n",
      "Epoch: 100, It: 100, Relative L2: 1.156e-01, Relative Linfty: 1.378e-01\n",
      "Epoch: 100, It: 100, Loss_helmholtz: 2.359e+04, Loss BCs(u): 1.081e+00\n",
      "Epoch: 100, It: 100, Lambda BCs: 6.377e+01, Lambda PDE: 2.112e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 150, It: 150, Time: 1.24s, Learning Rate: 4.7e-03\n",
      "Epoch: 150, It: 150, Total Error: 1.769e+04, Total Loss: 5.875e+01\n",
      "Epoch: 150, It: 150, Relative L2: 8.955e-02, Relative Linfty: 1.161e-01\n",
      "Epoch: 150, It: 150, Loss_helmholtz: 1.769e+04, Loss BCs(u): 8.105e-01\n",
      "Epoch: 150, It: 150, Lambda BCs: 6.377e+01, Lambda PDE: 3.088e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 200, It: 200, Time: 1.24s, Learning Rate: 4.7e-03\n",
      "Epoch: 200, It: 200, Total Error: 1.416e+04, Total Loss: 4.701e+01\n",
      "Epoch: 200, It: 200, Relative L2: 6.334e-02, Relative Linfty: 8.072e-02\n",
      "Epoch: 200, It: 200, Loss_helmholtz: 1.415e+04, Loss BCs(u): 6.484e-01\n",
      "Epoch: 200, It: 200, Lambda BCs: 6.377e+01, Lambda PDE: 4.269e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 250, It: 250, Time: 1.24s, Learning Rate: 4.6e-03\n",
      "Epoch: 250, It: 250, Total Error: 1.180e+04, Total Loss: 3.919e+01\n",
      "Epoch: 250, It: 250, Relative L2: 3.584e-02, Relative Linfty: 4.958e-02\n",
      "Epoch: 250, It: 250, Loss_helmholtz: 1.180e+04, Loss BCs(u): 5.403e-01\n",
      "Epoch: 250, It: 250, Lambda BCs: 6.377e+01, Lambda PDE: 5.424e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 300, It: 300, Time: 1.24s, Learning Rate: 4.5e-03\n",
      "Epoch: 300, It: 300, Total Error: 1.011e+04, Total Loss: 3.359e+01\n",
      "Epoch: 300, It: 300, Relative L2: 1.756e-02, Relative Linfty: 2.431e-02\n",
      "Epoch: 300, It: 300, Loss_helmholtz: 1.011e+04, Loss BCs(u): 4.631e-01\n",
      "Epoch: 300, It: 300, Lambda BCs: 6.377e+01, Lambda PDE: 5.802e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 350, It: 350, Time: 1.24s, Learning Rate: 4.4e-03\n",
      "Epoch: 350, It: 350, Total Error: 8.847e+03, Total Loss: 2.940e+01\n",
      "Epoch: 350, It: 350, Relative L2: 8.237e-03, Relative Linfty: 1.536e-02\n",
      "Epoch: 350, It: 350, Loss_helmholtz: 8.847e+03, Loss BCs(u): 4.052e-01\n",
      "Epoch: 350, It: 350, Lambda BCs: 6.377e+01, Lambda PDE: 6.222e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 400, It: 400, Time: 1.24s, Learning Rate: 4.3e-03\n",
      "Epoch: 400, It: 400, Total Error: 7.864e+03, Total Loss: 2.615e+01\n",
      "Epoch: 400, It: 400, Relative L2: 5.263e-03, Relative Linfty: 1.589e-02\n",
      "Epoch: 400, It: 400, Loss_helmholtz: 7.864e+03, Loss BCs(u): 3.602e-01\n",
      "Epoch: 400, It: 400, Lambda BCs: 6.377e+01, Lambda PDE: 6.641e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 450, It: 450, Time: 1.24s, Learning Rate: 4.3e-03\n",
      "Epoch: 450, It: 450, Total Error: 7.078e+03, Total Loss: 2.354e+01\n",
      "Epoch: 450, It: 450, Relative L2: 3.231e-03, Relative Linfty: 5.567e-03\n",
      "Epoch: 450, It: 450, Loss_helmholtz: 7.078e+03, Loss BCs(u): 3.242e-01\n",
      "Epoch: 450, It: 450, Lambda BCs: 6.377e+01, Lambda PDE: 7.054e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 500, It: 500, Time: 1.24s, Learning Rate: 4.2e-03\n",
      "Epoch: 500, It: 500, Total Error: 6.436e+03, Total Loss: 2.151e+01\n",
      "Epoch: 500, It: 500, Relative L2: 1.491e-02, Relative Linfty: 6.484e-02\n",
      "Epoch: 500, It: 500, Loss_helmholtz: 6.435e+03, Loss BCs(u): 2.947e-01\n",
      "Epoch: 500, It: 500, Lambda BCs: 6.377e+01, Lambda PDE: 7.411e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 550, It: 550, Time: 1.24s, Learning Rate: 4.1e-03\n",
      "Epoch: 550, It: 550, Total Error: 5.899e+03, Total Loss: 1.972e+01\n",
      "Epoch: 550, It: 550, Relative L2: 5.288e-03, Relative Linfty: 7.520e-03\n",
      "Epoch: 550, It: 550, Loss_helmholtz: 5.899e+03, Loss BCs(u): 2.702e-01\n",
      "Epoch: 550, It: 550, Lambda BCs: 6.377e+01, Lambda PDE: 7.894e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 600, It: 600, Time: 1.24s, Learning Rate: 4.0e-03\n",
      "Epoch: 600, It: 600, Total Error: 5.446e+03, Total Loss: 1.821e+01\n",
      "Epoch: 600, It: 600, Relative L2: 2.720e-03, Relative Linfty: 6.457e-03\n",
      "Epoch: 600, It: 600, Loss_helmholtz: 5.445e+03, Loss BCs(u): 2.494e-01\n",
      "Epoch: 600, It: 600, Lambda BCs: 6.377e+01, Lambda PDE: 8.337e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 650, It: 650, Time: 1.24s, Learning Rate: 4.0e-03\n",
      "Epoch: 650, It: 650, Total Error: 5.057e+03, Total Loss: 1.693e+01\n",
      "Epoch: 650, It: 650, Relative L2: 6.115e-03, Relative Linfty: 1.378e-02\n",
      "Epoch: 650, It: 650, Loss_helmholtz: 5.057e+03, Loss BCs(u): 2.316e-01\n",
      "Epoch: 650, It: 650, Lambda BCs: 6.377e+01, Lambda PDE: 8.767e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 700, It: 700, Time: 1.24s, Learning Rate: 3.9e-03\n",
      "Epoch: 700, It: 700, Total Error: 4.720e+03, Total Loss: 1.581e+01\n",
      "Epoch: 700, It: 700, Relative L2: 4.889e-03, Relative Linfty: 2.135e-02\n",
      "Epoch: 700, It: 700, Loss_helmholtz: 4.720e+03, Loss BCs(u): 2.161e-01\n",
      "Epoch: 700, It: 700, Lambda BCs: 6.377e+01, Lambda PDE: 9.255e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 750, It: 750, Time: 1.24s, Learning Rate: 3.8e-03\n",
      "Epoch: 750, It: 750, Total Error: 4.425e+03, Total Loss: 1.484e+01\n",
      "Epoch: 750, It: 750, Relative L2: 6.372e-03, Relative Linfty: 1.956e-02\n",
      "Epoch: 750, It: 750, Loss_helmholtz: 4.425e+03, Loss BCs(u): 2.026e-01\n",
      "Epoch: 750, It: 750, Lambda BCs: 6.377e+01, Lambda PDE: 9.687e-02\n",
      "--------------------------------------------------\n",
      "Epoch: 800, It: 800, Time: 1.24s, Learning Rate: 3.8e-03\n",
      "Epoch: 800, It: 800, Total Error: 4.165e+03, Total Loss: 1.397e+01\n",
      "Epoch: 800, It: 800, Relative L2: 4.494e-03, Relative Linfty: 6.717e-03\n",
      "Epoch: 800, It: 800, Loss_helmholtz: 4.165e+03, Loss BCs(u): 1.907e-01\n",
      "Epoch: 800, It: 800, Lambda BCs: 6.377e+01, Lambda PDE: 1.022e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 850, It: 850, Time: 1.24s, Learning Rate: 3.7e-03\n",
      "Epoch: 850, It: 850, Total Error: 3.934e+03, Total Loss: 1.321e+01\n",
      "Epoch: 850, It: 850, Relative L2: 7.576e-03, Relative Linfty: 2.643e-02\n",
      "Epoch: 850, It: 850, Loss_helmholtz: 3.933e+03, Loss BCs(u): 1.801e-01\n",
      "Epoch: 850, It: 850, Lambda BCs: 6.377e+01, Lambda PDE: 1.088e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 900, It: 900, Time: 1.23s, Learning Rate: 3.6e-03\n",
      "Epoch: 900, It: 900, Total Error: 3.727e+03, Total Loss: 1.252e+01\n",
      "Epoch: 900, It: 900, Relative L2: 2.983e-03, Relative Linfty: 5.132e-03\n",
      "Epoch: 900, It: 900, Loss_helmholtz: 3.726e+03, Loss BCs(u): 1.706e-01\n",
      "Epoch: 900, It: 900, Lambda BCs: 6.377e+01, Lambda PDE: 1.139e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 950, It: 950, Time: 1.24s, Learning Rate: 3.6e-03\n",
      "Epoch: 950, It: 950, Total Error: 3.540e+03, Total Loss: 1.191e+01\n",
      "Epoch: 950, It: 950, Relative L2: 4.462e-03, Relative Linfty: 1.177e-02\n",
      "Epoch: 950, It: 950, Loss_helmholtz: 3.540e+03, Loss BCs(u): 1.621e-01\n",
      "Epoch: 950, It: 950, Lambda BCs: 6.377e+01, Lambda PDE: 1.185e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1000, It: 1000, Time: 1.24s, Learning Rate: 3.5e-03\n",
      "Epoch: 1000, It: 1000, Total Error: 3.372e+03, Total Loss: 1.138e+01\n",
      "Epoch: 1000, It: 1000, Relative L2: 8.041e-03, Relative Linfty: 2.636e-02\n",
      "Epoch: 1000, It: 1000, Loss_helmholtz: 3.372e+03, Loss BCs(u): 1.544e-01\n",
      "Epoch: 1000, It: 1000, Lambda BCs: 6.377e+01, Lambda PDE: 1.228e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1050, It: 1050, Time: 1.25s, Learning Rate: 3.4e-03\n",
      "Epoch: 1050, It: 1050, Total Error: 3.219e+03, Total Loss: 1.086e+01\n",
      "Epoch: 1050, It: 1050, Relative L2: 1.603e-03, Relative Linfty: 4.072e-03\n",
      "Epoch: 1050, It: 1050, Loss_helmholtz: 3.219e+03, Loss BCs(u): 1.474e-01\n",
      "Epoch: 1050, It: 1050, Lambda BCs: 6.377e+01, Lambda PDE: 1.278e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1100, It: 1100, Time: 1.24s, Learning Rate: 3.4e-03\n",
      "Epoch: 1100, It: 1100, Total Error: 3.079e+03, Total Loss: 1.039e+01\n",
      "Epoch: 1100, It: 1100, Relative L2: 3.018e-03, Relative Linfty: 7.981e-03\n",
      "Epoch: 1100, It: 1100, Loss_helmholtz: 3.079e+03, Loss BCs(u): 1.410e-01\n",
      "Epoch: 1100, It: 1100, Lambda BCs: 6.377e+01, Lambda PDE: 1.325e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1150, It: 1150, Time: 1.23s, Learning Rate: 3.3e-03\n",
      "Epoch: 1150, It: 1150, Total Error: 2.951e+03, Total Loss: 9.998e+00\n",
      "Epoch: 1150, It: 1150, Relative L2: 7.417e-03, Relative Linfty: 2.607e-02\n",
      "Epoch: 1150, It: 1150, Loss_helmholtz: 2.951e+03, Loss BCs(u): 1.351e-01\n",
      "Epoch: 1150, It: 1150, Lambda BCs: 6.377e+01, Lambda PDE: 1.370e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1200, It: 1200, Time: 1.24s, Learning Rate: 3.3e-03\n",
      "Epoch: 1200, It: 1200, Total Error: 2.833e+03, Total Loss: 9.604e+00\n",
      "Epoch: 1200, It: 1200, Relative L2: 4.248e-03, Relative Linfty: 1.361e-02\n",
      "Epoch: 1200, It: 1200, Loss_helmholtz: 2.833e+03, Loss BCs(u): 1.297e-01\n",
      "Epoch: 1200, It: 1200, Lambda BCs: 6.377e+01, Lambda PDE: 1.415e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1250, It: 1250, Time: 1.24s, Learning Rate: 3.2e-03\n",
      "Epoch: 1250, It: 1250, Total Error: 2.724e+03, Total Loss: 9.236e+00\n",
      "Epoch: 1250, It: 1250, Relative L2: 1.645e-03, Relative Linfty: 3.409e-03\n",
      "Epoch: 1250, It: 1250, Loss_helmholtz: 2.724e+03, Loss BCs(u): 1.247e-01\n",
      "Epoch: 1250, It: 1250, Lambda BCs: 6.377e+01, Lambda PDE: 1.469e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1300, It: 1300, Time: 1.24s, Learning Rate: 3.1e-03\n",
      "Epoch: 1300, It: 1300, Total Error: 2.623e+03, Total Loss: 8.896e+00\n",
      "Epoch: 1300, It: 1300, Relative L2: 3.083e-03, Relative Linfty: 6.427e-03\n",
      "Epoch: 1300, It: 1300, Loss_helmholtz: 2.623e+03, Loss BCs(u): 1.201e-01\n",
      "Epoch: 1300, It: 1300, Lambda BCs: 6.377e+01, Lambda PDE: 1.504e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1350, It: 1350, Time: 1.23s, Learning Rate: 3.1e-03\n",
      "Epoch: 1350, It: 1350, Total Error: 2.529e+03, Total Loss: 8.579e+00\n",
      "Epoch: 1350, It: 1350, Relative L2: 1.545e-03, Relative Linfty: 3.616e-03\n",
      "Epoch: 1350, It: 1350, Loss_helmholtz: 2.529e+03, Loss BCs(u): 1.158e-01\n",
      "Epoch: 1350, It: 1350, Lambda BCs: 6.377e+01, Lambda PDE: 1.555e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1400, It: 1400, Time: 1.23s, Learning Rate: 3.0e-03\n",
      "Epoch: 1400, It: 1400, Total Error: 2.442e+03, Total Loss: 8.297e+00\n",
      "Epoch: 1400, It: 1400, Relative L2: 4.741e-03, Relative Linfty: 1.259e-02\n",
      "Epoch: 1400, It: 1400, Loss_helmholtz: 2.442e+03, Loss BCs(u): 1.118e-01\n",
      "Epoch: 1400, It: 1400, Lambda BCs: 6.377e+01, Lambda PDE: 1.603e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1450, It: 1450, Time: 1.23s, Learning Rate: 3.0e-03\n",
      "Epoch: 1450, It: 1450, Total Error: 2.361e+03, Total Loss: 8.022e+00\n",
      "Epoch: 1450, It: 1450, Relative L2: 2.004e-03, Relative Linfty: 4.239e-03\n",
      "Epoch: 1450, It: 1450, Loss_helmholtz: 2.361e+03, Loss BCs(u): 1.081e-01\n",
      "Epoch: 1450, It: 1450, Lambda BCs: 6.377e+01, Lambda PDE: 1.660e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1500, It: 1500, Time: 1.24s, Learning Rate: 2.9e-03\n",
      "Epoch: 1500, It: 1500, Total Error: 2.285e+03, Total Loss: 7.764e+00\n",
      "Epoch: 1500, It: 1500, Relative L2: 1.601e-03, Relative Linfty: 3.761e-03\n",
      "Epoch: 1500, It: 1500, Loss_helmholtz: 2.284e+03, Loss BCs(u): 1.046e-01\n",
      "Epoch: 1500, It: 1500, Lambda BCs: 6.377e+01, Lambda PDE: 1.710e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1550, It: 1550, Time: 1.24s, Learning Rate: 2.9e-03\n",
      "Epoch: 1550, It: 1550, Total Error: 2.213e+03, Total Loss: 7.532e+00\n",
      "Epoch: 1550, It: 1550, Relative L2: 6.218e-03, Relative Linfty: 2.596e-02\n",
      "Epoch: 1550, It: 1550, Loss_helmholtz: 2.213e+03, Loss BCs(u): 1.013e-01\n",
      "Epoch: 1550, It: 1550, Lambda BCs: 6.377e+01, Lambda PDE: 1.755e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1600, It: 1600, Time: 1.24s, Learning Rate: 2.8e-03\n",
      "Epoch: 1600, It: 1600, Total Error: 2.146e+03, Total Loss: 7.304e+00\n",
      "Epoch: 1600, It: 1600, Relative L2: 1.504e-03, Relative Linfty: 3.217e-03\n",
      "Epoch: 1600, It: 1600, Loss_helmholtz: 2.146e+03, Loss BCs(u): 9.825e-02\n",
      "Epoch: 1600, It: 1600, Lambda BCs: 6.377e+01, Lambda PDE: 1.811e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1650, It: 1650, Time: 1.24s, Learning Rate: 2.8e-03\n",
      "Epoch: 1650, It: 1650, Total Error: 2.083e+03, Total Loss: 7.091e+00\n",
      "Epoch: 1650, It: 1650, Relative L2: 1.675e-03, Relative Linfty: 3.890e-03\n",
      "Epoch: 1650, It: 1650, Loss_helmholtz: 2.083e+03, Loss BCs(u): 9.536e-02\n",
      "Epoch: 1650, It: 1650, Lambda BCs: 6.377e+01, Lambda PDE: 1.847e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1700, It: 1700, Time: 1.23s, Learning Rate: 2.7e-03\n",
      "Epoch: 1700, It: 1700, Total Error: 2.024e+03, Total Loss: 6.900e+00\n",
      "Epoch: 1700, It: 1700, Relative L2: 7.945e-03, Relative Linfty: 2.329e-02\n",
      "Epoch: 1700, It: 1700, Loss_helmholtz: 2.023e+03, Loss BCs(u): 9.264e-02\n",
      "Epoch: 1700, It: 1700, Lambda BCs: 6.377e+01, Lambda PDE: 1.902e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1750, It: 1750, Time: 1.23s, Learning Rate: 2.7e-03\n",
      "Epoch: 1750, It: 1750, Total Error: 1.967e+03, Total Loss: 6.709e+00\n",
      "Epoch: 1750, It: 1750, Relative L2: 1.304e-03, Relative Linfty: 2.419e-03\n",
      "Epoch: 1750, It: 1750, Loss_helmholtz: 1.967e+03, Loss BCs(u): 9.006e-02\n",
      "Epoch: 1750, It: 1750, Lambda BCs: 6.377e+01, Lambda PDE: 1.960e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1800, It: 1800, Time: 1.23s, Learning Rate: 2.6e-03\n",
      "Epoch: 1800, It: 1800, Total Error: 1.914e+03, Total Loss: 6.545e+00\n",
      "Epoch: 1800, It: 1800, Relative L2: 5.879e-03, Relative Linfty: 1.448e-02\n",
      "Epoch: 1800, It: 1800, Loss_helmholtz: 1.914e+03, Loss BCs(u): 8.763e-02\n",
      "Epoch: 1800, It: 1800, Lambda BCs: 6.377e+01, Lambda PDE: 2.020e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1850, It: 1850, Time: 1.23s, Learning Rate: 2.6e-03\n",
      "Epoch: 1850, It: 1850, Total Error: 1.864e+03, Total Loss: 6.373e+00\n",
      "Epoch: 1850, It: 1850, Relative L2: 1.135e-03, Relative Linfty: 2.286e-03\n",
      "Epoch: 1850, It: 1850, Loss_helmholtz: 1.864e+03, Loss BCs(u): 8.533e-02\n",
      "Epoch: 1850, It: 1850, Lambda BCs: 6.377e+01, Lambda PDE: 2.080e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1900, It: 1900, Time: 1.23s, Learning Rate: 2.5e-03\n",
      "Epoch: 1900, It: 1900, Total Error: 1.816e+03, Total Loss: 6.210e+00\n",
      "Epoch: 1900, It: 1900, Relative L2: 1.366e-03, Relative Linfty: 3.329e-03\n",
      "Epoch: 1900, It: 1900, Loss_helmholtz: 1.816e+03, Loss BCs(u): 8.314e-02\n",
      "Epoch: 1900, It: 1900, Lambda BCs: 6.377e+01, Lambda PDE: 2.129e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 1950, It: 1950, Time: 1.24s, Learning Rate: 2.5e-03\n",
      "Epoch: 1950, It: 1950, Total Error: 1.771e+03, Total Loss: 6.077e+00\n",
      "Epoch: 1950, It: 1950, Relative L2: 4.514e-03, Relative Linfty: 1.497e-02\n",
      "Epoch: 1950, It: 1950, Loss_helmholtz: 1.771e+03, Loss BCs(u): 8.106e-02\n",
      "Epoch: 1950, It: 1950, Lambda BCs: 6.377e+01, Lambda PDE: 2.177e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2000, It: 2000, Time: 1.24s, Learning Rate: 2.4e-03\n",
      "Epoch: 2000, It: 2000, Total Error: 1.728e+03, Total Loss: 5.935e+00\n",
      "Epoch: 2000, It: 2000, Relative L2: 4.381e-03, Relative Linfty: 1.069e-02\n",
      "Epoch: 2000, It: 2000, Loss_helmholtz: 1.727e+03, Loss BCs(u): 7.908e-02\n",
      "Epoch: 2000, It: 2000, Lambda BCs: 6.377e+01, Lambda PDE: 2.224e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2050, It: 2050, Time: 1.24s, Learning Rate: 2.4e-03\n",
      "Epoch: 2050, It: 2050, Total Error: 1.686e+03, Total Loss: 5.795e+00\n",
      "Epoch: 2050, It: 2050, Relative L2: 1.847e-03, Relative Linfty: 5.352e-03\n",
      "Epoch: 2050, It: 2050, Loss_helmholtz: 1.686e+03, Loss BCs(u): 7.720e-02\n",
      "Epoch: 2050, It: 2050, Lambda BCs: 6.377e+01, Lambda PDE: 2.276e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2100, It: 2100, Time: 1.24s, Learning Rate: 2.4e-03\n",
      "Epoch: 2100, It: 2100, Total Error: 1.647e+03, Total Loss: 5.661e+00\n",
      "Epoch: 2100, It: 2100, Relative L2: 1.337e-03, Relative Linfty: 2.915e-03\n",
      "Epoch: 2100, It: 2100, Loss_helmholtz: 1.647e+03, Loss BCs(u): 7.540e-02\n",
      "Epoch: 2100, It: 2100, Lambda BCs: 6.377e+01, Lambda PDE: 2.316e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2150, It: 2150, Time: 1.24s, Learning Rate: 2.3e-03\n",
      "Epoch: 2150, It: 2150, Total Error: 1.610e+03, Total Loss: 5.533e+00\n",
      "Epoch: 2150, It: 2150, Relative L2: 1.531e-03, Relative Linfty: 3.637e-03\n",
      "Epoch: 2150, It: 2150, Loss_helmholtz: 1.610e+03, Loss BCs(u): 7.369e-02\n",
      "Epoch: 2150, It: 2150, Lambda BCs: 6.377e+01, Lambda PDE: 2.350e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2200, It: 2200, Time: 1.24s, Learning Rate: 2.3e-03\n",
      "Epoch: 2200, It: 2200, Total Error: 1.574e+03, Total Loss: 5.412e+00\n",
      "Epoch: 2200, It: 2200, Relative L2: 2.417e-03, Relative Linfty: 6.288e-03\n",
      "Epoch: 2200, It: 2200, Loss_helmholtz: 1.574e+03, Loss BCs(u): 7.205e-02\n",
      "Epoch: 2200, It: 2200, Lambda BCs: 6.377e+01, Lambda PDE: 2.393e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2250, It: 2250, Time: 1.24s, Learning Rate: 2.2e-03\n",
      "Epoch: 2250, It: 2250, Total Error: 1.540e+03, Total Loss: 5.308e+00\n",
      "Epoch: 2250, It: 2250, Relative L2: 4.504e-03, Relative Linfty: 1.543e-02\n",
      "Epoch: 2250, It: 2250, Loss_helmholtz: 1.540e+03, Loss BCs(u): 7.049e-02\n",
      "Epoch: 2250, It: 2250, Lambda BCs: 6.377e+01, Lambda PDE: 2.437e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2300, It: 2300, Time: 1.24s, Learning Rate: 2.2e-03\n",
      "Epoch: 2300, It: 2300, Total Error: 1.507e+03, Total Loss: 5.201e+00\n",
      "Epoch: 2300, It: 2300, Relative L2: 3.651e-03, Relative Linfty: 1.034e-02\n",
      "Epoch: 2300, It: 2300, Loss_helmholtz: 1.507e+03, Loss BCs(u): 6.899e-02\n",
      "Epoch: 2300, It: 2300, Lambda BCs: 6.377e+01, Lambda PDE: 2.480e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2350, It: 2350, Time: 1.24s, Learning Rate: 2.2e-03\n",
      "Epoch: 2350, It: 2350, Total Error: 1.476e+03, Total Loss: 5.103e+00\n",
      "Epoch: 2350, It: 2350, Relative L2: 5.125e-03, Relative Linfty: 2.083e-02\n",
      "Epoch: 2350, It: 2350, Loss_helmholtz: 1.476e+03, Loss BCs(u): 6.755e-02\n",
      "Epoch: 2350, It: 2350, Lambda BCs: 6.377e+01, Lambda PDE: 2.523e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2400, It: 2400, Time: 1.24s, Learning Rate: 2.1e-03\n",
      "Epoch: 2400, It: 2400, Total Error: 1.446e+03, Total Loss: 5.000e+00\n",
      "Epoch: 2400, It: 2400, Relative L2: 1.369e-03, Relative Linfty: 3.012e-03\n",
      "Epoch: 2400, It: 2400, Loss_helmholtz: 1.446e+03, Loss BCs(u): 6.618e-02\n",
      "Epoch: 2400, It: 2400, Lambda BCs: 6.377e+01, Lambda PDE: 2.558e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2450, It: 2450, Time: 1.24s, Learning Rate: 2.1e-03\n",
      "Epoch: 2450, It: 2450, Total Error: 1.417e+03, Total Loss: 4.901e+00\n",
      "Epoch: 2450, It: 2450, Relative L2: 1.406e-03, Relative Linfty: 5.022e-03\n",
      "Epoch: 2450, It: 2450, Loss_helmholtz: 1.417e+03, Loss BCs(u): 6.485e-02\n",
      "Epoch: 2450, It: 2450, Lambda BCs: 6.377e+01, Lambda PDE: 2.595e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2500, It: 2500, Time: 1.24s, Learning Rate: 2.0e-03\n",
      "Epoch: 2500, It: 2500, Total Error: 1.389e+03, Total Loss: 4.805e+00\n",
      "Epoch: 2500, It: 2500, Relative L2: 8.684e-04, Relative Linfty: 2.765e-03\n",
      "Epoch: 2500, It: 2500, Loss_helmholtz: 1.389e+03, Loss BCs(u): 6.358e-02\n",
      "Epoch: 2500, It: 2500, Lambda BCs: 6.377e+01, Lambda PDE: 2.629e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2550, It: 2550, Time: 1.25s, Learning Rate: 2.0e-03\n",
      "Epoch: 2550, It: 2550, Total Error: 1.362e+03, Total Loss: 4.713e+00\n",
      "Epoch: 2550, It: 2550, Relative L2: 2.293e-03, Relative Linfty: 4.633e-03\n",
      "Epoch: 2550, It: 2550, Loss_helmholtz: 1.362e+03, Loss BCs(u): 6.236e-02\n",
      "Epoch: 2550, It: 2550, Lambda BCs: 6.377e+01, Lambda PDE: 2.660e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2600, It: 2600, Time: 1.24s, Learning Rate: 2.0e-03\n",
      "Epoch: 2600, It: 2600, Total Error: 1.337e+03, Total Loss: 4.637e+00\n",
      "Epoch: 2600, It: 2600, Relative L2: 5.204e-03, Relative Linfty: 2.415e-02\n",
      "Epoch: 2600, It: 2600, Loss_helmholtz: 1.336e+03, Loss BCs(u): 6.118e-02\n",
      "Epoch: 2600, It: 2600, Lambda BCs: 6.377e+01, Lambda PDE: 2.690e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2650, It: 2650, Time: 1.24s, Learning Rate: 1.9e-03\n",
      "Epoch: 2650, It: 2650, Total Error: 1.312e+03, Total Loss: 4.553e+00\n",
      "Epoch: 2650, It: 2650, Relative L2: 2.035e-03, Relative Linfty: 7.219e-03\n",
      "Epoch: 2650, It: 2650, Loss_helmholtz: 1.312e+03, Loss BCs(u): 6.005e-02\n",
      "Epoch: 2650, It: 2650, Lambda BCs: 6.377e+01, Lambda PDE: 2.730e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2700, It: 2700, Time: 1.24s, Learning Rate: 1.9e-03\n",
      "Epoch: 2700, It: 2700, Total Error: 1.288e+03, Total Loss: 4.472e+00\n",
      "Epoch: 2700, It: 2700, Relative L2: 2.162e-03, Relative Linfty: 6.647e-03\n",
      "Epoch: 2700, It: 2700, Loss_helmholtz: 1.288e+03, Loss BCs(u): 5.896e-02\n",
      "Epoch: 2700, It: 2700, Lambda BCs: 6.377e+01, Lambda PDE: 2.767e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2750, It: 2750, Time: 1.24s, Learning Rate: 1.9e-03\n",
      "Epoch: 2750, It: 2750, Total Error: 1.265e+03, Total Loss: 4.393e+00\n",
      "Epoch: 2750, It: 2750, Relative L2: 1.338e-03, Relative Linfty: 2.854e-03\n",
      "Epoch: 2750, It: 2750, Loss_helmholtz: 1.265e+03, Loss BCs(u): 5.790e-02\n",
      "Epoch: 2750, It: 2750, Lambda BCs: 6.377e+01, Lambda PDE: 2.801e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2800, It: 2800, Time: 1.24s, Learning Rate: 1.8e-03\n",
      "Epoch: 2800, It: 2800, Total Error: 1.243e+03, Total Loss: 4.317e+00\n",
      "Epoch: 2800, It: 2800, Relative L2: 1.792e-03, Relative Linfty: 4.226e-03\n",
      "Epoch: 2800, It: 2800, Loss_helmholtz: 1.243e+03, Loss BCs(u): 5.689e-02\n",
      "Epoch: 2800, It: 2800, Lambda BCs: 6.377e+01, Lambda PDE: 2.840e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2850, It: 2850, Time: 1.24s, Learning Rate: 1.8e-03\n",
      "Epoch: 2850, It: 2850, Total Error: 1.221e+03, Total Loss: 4.243e+00\n",
      "Epoch: 2850, It: 2850, Relative L2: 1.273e-03, Relative Linfty: 3.185e-03\n",
      "Epoch: 2850, It: 2850, Loss_helmholtz: 1.221e+03, Loss BCs(u): 5.591e-02\n",
      "Epoch: 2850, It: 2850, Lambda BCs: 6.377e+01, Lambda PDE: 2.868e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2900, It: 2900, Time: 1.24s, Learning Rate: 1.8e-03\n",
      "Epoch: 2900, It: 2900, Total Error: 1.201e+03, Total Loss: 4.172e+00\n",
      "Epoch: 2900, It: 2900, Relative L2: 1.283e-03, Relative Linfty: 3.448e-03\n",
      "Epoch: 2900, It: 2900, Loss_helmholtz: 1.201e+03, Loss BCs(u): 5.496e-02\n",
      "Epoch: 2900, It: 2900, Lambda BCs: 6.377e+01, Lambda PDE: 2.901e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 2950, It: 2950, Time: 1.24s, Learning Rate: 1.7e-03\n",
      "Epoch: 2950, It: 2950, Total Error: 1.181e+03, Total Loss: 4.104e+00\n",
      "Epoch: 2950, It: 2950, Relative L2: 1.575e-03, Relative Linfty: 3.829e-03\n",
      "Epoch: 2950, It: 2950, Loss_helmholtz: 1.181e+03, Loss BCs(u): 5.404e-02\n",
      "Epoch: 2950, It: 2950, Lambda BCs: 6.377e+01, Lambda PDE: 2.939e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3000, It: 3000, Time: 1.24s, Learning Rate: 1.7e-03\n",
      "Epoch: 3000, It: 3000, Total Error: 1.161e+03, Total Loss: 4.039e+00\n",
      "Epoch: 3000, It: 3000, Relative L2: 2.981e-03, Relative Linfty: 1.162e-02\n",
      "Epoch: 3000, It: 3000, Loss_helmholtz: 1.161e+03, Loss BCs(u): 5.316e-02\n",
      "Epoch: 3000, It: 3000, Lambda BCs: 6.377e+01, Lambda PDE: 2.976e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3050, It: 3050, Time: 1.24s, Learning Rate: 1.7e-03\n",
      "Epoch: 3050, It: 3050, Total Error: 1.143e+03, Total Loss: 3.977e+00\n",
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      "Epoch: 3050, It: 3050, Loss_helmholtz: 1.143e+03, Loss BCs(u): 5.230e-02\n",
      "Epoch: 3050, It: 3050, Lambda BCs: 6.377e+01, Lambda PDE: 3.008e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3100, It: 3100, Time: 1.24s, Learning Rate: 1.7e-03\n",
      "Epoch: 3100, It: 3100, Total Error: 1.124e+03, Total Loss: 3.914e+00\n",
      "Epoch: 3100, It: 3100, Relative L2: 1.216e-03, Relative Linfty: 3.480e-03\n",
      "Epoch: 3100, It: 3100, Loss_helmholtz: 1.124e+03, Loss BCs(u): 5.147e-02\n",
      "Epoch: 3100, It: 3100, Lambda BCs: 6.377e+01, Lambda PDE: 3.036e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3150, It: 3150, Time: 1.24s, Learning Rate: 1.6e-03\n",
      "Epoch: 3150, It: 3150, Total Error: 1.107e+03, Total Loss: 3.854e+00\n",
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      "Epoch: 3150, It: 3150, Loss_helmholtz: 1.107e+03, Loss BCs(u): 5.067e-02\n",
      "Epoch: 3150, It: 3150, Lambda BCs: 6.377e+01, Lambda PDE: 3.074e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3200, It: 3200, Time: 1.24s, Learning Rate: 1.6e-03\n",
      "Epoch: 3200, It: 3200, Total Error: 1.090e+03, Total Loss: 3.797e+00\n",
      "Epoch: 3200, It: 3200, Relative L2: 2.118e-03, Relative Linfty: 9.653e-03\n",
      "Epoch: 3200, It: 3200, Loss_helmholtz: 1.090e+03, Loss BCs(u): 4.989e-02\n",
      "Epoch: 3200, It: 3200, Lambda BCs: 6.377e+01, Lambda PDE: 3.106e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3250, It: 3250, Time: 1.24s, Learning Rate: 1.6e-03\n",
      "Epoch: 3250, It: 3250, Total Error: 1.073e+03, Total Loss: 3.741e+00\n",
      "Epoch: 3250, It: 3250, Relative L2: 1.681e-03, Relative Linfty: 6.718e-03\n",
      "Epoch: 3250, It: 3250, Loss_helmholtz: 1.073e+03, Loss BCs(u): 4.913e-02\n",
      "Epoch: 3250, It: 3250, Lambda BCs: 6.377e+01, Lambda PDE: 3.139e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3300, It: 3300, Time: 1.24s, Learning Rate: 1.5e-03\n",
      "Epoch: 3300, It: 3300, Total Error: 1.057e+03, Total Loss: 3.686e+00\n",
      "Epoch: 3300, It: 3300, Relative L2: 1.492e-03, Relative Linfty: 3.365e-03\n",
      "Epoch: 3300, It: 3300, Loss_helmholtz: 1.057e+03, Loss BCs(u): 4.840e-02\n",
      "Epoch: 3300, It: 3300, Lambda BCs: 6.377e+01, Lambda PDE: 3.167e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3350, It: 3350, Time: 1.24s, Learning Rate: 1.5e-03\n",
      "Epoch: 3350, It: 3350, Total Error: 1.042e+03, Total Loss: 3.633e+00\n",
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      "Epoch: 3350, It: 3350, Loss_helmholtz: 1.042e+03, Loss BCs(u): 4.769e-02\n",
      "Epoch: 3350, It: 3350, Lambda BCs: 6.377e+01, Lambda PDE: 3.191e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3400, It: 3400, Time: 1.25s, Learning Rate: 1.5e-03\n",
      "Epoch: 3400, It: 3400, Total Error: 1.027e+03, Total Loss: 3.582e+00\n",
      "Epoch: 3400, It: 3400, Relative L2: 1.452e-03, Relative Linfty: 4.956e-03\n",
      "Epoch: 3400, It: 3400, Loss_helmholtz: 1.027e+03, Loss BCs(u): 4.700e-02\n",
      "Epoch: 3400, It: 3400, Lambda BCs: 6.377e+01, Lambda PDE: 3.216e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3450, It: 3450, Time: 1.24s, Learning Rate: 1.5e-03\n",
      "Epoch: 3450, It: 3450, Total Error: 1.012e+03, Total Loss: 3.532e+00\n",
      "Epoch: 3450, It: 3450, Relative L2: 1.567e-03, Relative Linfty: 5.643e-03\n",
      "Epoch: 3450, It: 3450, Loss_helmholtz: 1.012e+03, Loss BCs(u): 4.633e-02\n",
      "Epoch: 3450, It: 3450, Lambda BCs: 6.377e+01, Lambda PDE: 3.235e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3500, It: 3500, Time: 1.24s, Learning Rate: 1.4e-03\n",
      "Epoch: 3500, It: 3500, Total Error: 9.978e+02, Total Loss: 3.483e+00\n",
      "Epoch: 3500, It: 3500, Relative L2: 1.224e-03, Relative Linfty: 3.828e-03\n",
      "Epoch: 3500, It: 3500, Loss_helmholtz: 9.977e+02, Loss BCs(u): 4.567e-02\n",
      "Epoch: 3500, It: 3500, Lambda BCs: 6.377e+01, Lambda PDE: 3.265e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3550, It: 3550, Time: 1.24s, Learning Rate: 1.4e-03\n",
      "Epoch: 3550, It: 3550, Total Error: 9.839e+02, Total Loss: 3.435e+00\n",
      "Epoch: 3550, It: 3550, Relative L2: 9.020e-04, Relative Linfty: 2.772e-03\n",
      "Epoch: 3550, It: 3550, Loss_helmholtz: 9.839e+02, Loss BCs(u): 4.504e-02\n",
      "Epoch: 3550, It: 3550, Lambda BCs: 6.377e+01, Lambda PDE: 3.293e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3600, It: 3600, Time: 1.24s, Learning Rate: 1.4e-03\n",
      "Epoch: 3600, It: 3600, Total Error: 9.704e+02, Total Loss: 3.390e+00\n",
      "Epoch: 3600, It: 3600, Relative L2: 1.512e-03, Relative Linfty: 4.601e-03\n",
      "Epoch: 3600, It: 3600, Loss_helmholtz: 9.704e+02, Loss BCs(u): 4.442e-02\n",
      "Epoch: 3600, It: 3600, Lambda BCs: 6.377e+01, Lambda PDE: 3.323e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3650, It: 3650, Time: 1.24s, Learning Rate: 1.4e-03\n",
      "Epoch: 3650, It: 3650, Total Error: 9.573e+02, Total Loss: 3.345e+00\n",
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      "Epoch: 3650, It: 3650, Loss_helmholtz: 9.573e+02, Loss BCs(u): 4.382e-02\n",
      "Epoch: 3650, It: 3650, Lambda BCs: 6.377e+01, Lambda PDE: 3.350e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3700, It: 3700, Time: 1.24s, Learning Rate: 1.3e-03\n",
      "Epoch: 3700, It: 3700, Total Error: 9.446e+02, Total Loss: 3.302e+00\n",
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      "Epoch: 3700, It: 3700, Loss_helmholtz: 9.445e+02, Loss BCs(u): 4.324e-02\n",
      "Epoch: 3700, It: 3700, Lambda BCs: 6.377e+01, Lambda PDE: 3.382e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3750, It: 3750, Time: 1.24s, Learning Rate: 1.3e-03\n",
      "Epoch: 3750, It: 3750, Total Error: 9.321e+02, Total Loss: 3.259e+00\n",
      "Epoch: 3750, It: 3750, Relative L2: 1.309e-03, Relative Linfty: 5.481e-03\n",
      "Epoch: 3750, It: 3750, Loss_helmholtz: 9.321e+02, Loss BCs(u): 4.267e-02\n",
      "Epoch: 3750, It: 3750, Lambda BCs: 6.377e+01, Lambda PDE: 3.403e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3800, It: 3800, Time: 1.24s, Learning Rate: 1.3e-03\n",
      "Epoch: 3800, It: 3800, Total Error: 9.200e+02, Total Loss: 3.218e+00\n",
      "Epoch: 3800, It: 3800, Relative L2: 1.099e-03, Relative Linfty: 3.754e-03\n",
      "Epoch: 3800, It: 3800, Loss_helmholtz: 9.200e+02, Loss BCs(u): 4.211e-02\n",
      "Epoch: 3800, It: 3800, Lambda BCs: 6.377e+01, Lambda PDE: 3.422e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3850, It: 3850, Time: 1.24s, Learning Rate: 1.3e-03\n",
      "Epoch: 3850, It: 3850, Total Error: 9.082e+02, Total Loss: 3.176e+00\n",
      "Epoch: 3850, It: 3850, Relative L2: 6.884e-04, Relative Linfty: 1.694e-03\n",
      "Epoch: 3850, It: 3850, Loss_helmholtz: 9.082e+02, Loss BCs(u): 4.157e-02\n",
      "Epoch: 3850, It: 3850, Lambda BCs: 6.377e+01, Lambda PDE: 3.443e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3900, It: 3900, Time: 1.24s, Learning Rate: 1.2e-03\n",
      "Epoch: 3900, It: 3900, Total Error: 8.967e+02, Total Loss: 3.138e+00\n",
      "Epoch: 3900, It: 3900, Relative L2: 2.331e-03, Relative Linfty: 7.156e-03\n",
      "Epoch: 3900, It: 3900, Loss_helmholtz: 8.967e+02, Loss BCs(u): 4.105e-02\n",
      "Epoch: 3900, It: 3900, Lambda BCs: 6.377e+01, Lambda PDE: 3.476e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 3950, It: 3950, Time: 1.24s, Learning Rate: 1.2e-03\n",
      "Epoch: 3950, It: 3950, Total Error: 8.855e+02, Total Loss: 3.099e+00\n",
      "Epoch: 3950, It: 3950, Relative L2: 6.422e-04, Relative Linfty: 2.611e-03\n",
      "Epoch: 3950, It: 3950, Loss_helmholtz: 8.855e+02, Loss BCs(u): 4.053e-02\n",
      "Epoch: 3950, It: 3950, Lambda BCs: 6.377e+01, Lambda PDE: 3.502e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4000, It: 4000, Time: 1.24s, Learning Rate: 1.2e-03\n",
      "Epoch: 4000, It: 4000, Total Error: 8.746e+02, Total Loss: 3.062e+00\n",
      "Epoch: 4000, It: 4000, Relative L2: 1.445e-03, Relative Linfty: 5.056e-03\n",
      "Epoch: 4000, It: 4000, Loss_helmholtz: 8.746e+02, Loss BCs(u): 4.003e-02\n",
      "Epoch: 4000, It: 4000, Lambda BCs: 6.377e+01, Lambda PDE: 3.522e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4050, It: 4050, Time: 1.24s, Learning Rate: 1.2e-03\n",
      "Epoch: 4050, It: 4050, Total Error: 8.639e+02, Total Loss: 3.025e+00\n",
      "Epoch: 4050, It: 4050, Relative L2: 9.397e-04, Relative Linfty: 3.233e-03\n",
      "Epoch: 4050, It: 4050, Loss_helmholtz: 8.639e+02, Loss BCs(u): 3.955e-02\n",
      "Epoch: 4050, It: 4050, Lambda BCs: 6.377e+01, Lambda PDE: 3.546e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4100, It: 4100, Time: 1.24s, Learning Rate: 1.2e-03\n",
      "Epoch: 4100, It: 4100, Total Error: 8.535e+02, Total Loss: 2.989e+00\n",
      "Epoch: 4100, It: 4100, Relative L2: 1.125e-03, Relative Linfty: 3.803e-03\n",
      "Epoch: 4100, It: 4100, Loss_helmholtz: 8.535e+02, Loss BCs(u): 3.907e-02\n",
      "Epoch: 4100, It: 4100, Lambda BCs: 6.377e+01, Lambda PDE: 3.567e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4150, It: 4150, Time: 1.24s, Learning Rate: 1.1e-03\n",
      "Epoch: 4150, It: 4150, Total Error: 8.434e+02, Total Loss: 2.954e+00\n",
      "Epoch: 4150, It: 4150, Relative L2: 6.829e-04, Relative Linfty: 2.063e-03\n",
      "Epoch: 4150, It: 4150, Loss_helmholtz: 8.433e+02, Loss BCs(u): 3.860e-02\n",
      "Epoch: 4150, It: 4150, Lambda BCs: 6.377e+01, Lambda PDE: 3.591e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4200, It: 4200, Time: 1.24s, Learning Rate: 1.1e-03\n",
      "Epoch: 4200, It: 4200, Total Error: 8.334e+02, Total Loss: 2.919e+00\n",
      "Epoch: 4200, It: 4200, Relative L2: 1.113e-03, Relative Linfty: 3.956e-03\n",
      "Epoch: 4200, It: 4200, Loss_helmholtz: 8.334e+02, Loss BCs(u): 3.815e-02\n",
      "Epoch: 4200, It: 4200, Lambda BCs: 6.377e+01, Lambda PDE: 3.614e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4250, It: 4250, Time: 1.24s, Learning Rate: 1.1e-03\n",
      "Epoch: 4250, It: 4250, Total Error: 8.238e+02, Total Loss: 2.886e+00\n",
      "Epoch: 4250, It: 4250, Relative L2: 8.846e-04, Relative Linfty: 3.306e-03\n",
      "Epoch: 4250, It: 4250, Loss_helmholtz: 8.237e+02, Loss BCs(u): 3.771e-02\n",
      "Epoch: 4250, It: 4250, Lambda BCs: 6.377e+01, Lambda PDE: 3.636e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4300, It: 4300, Time: 1.24s, Learning Rate: 1.1e-03\n",
      "Epoch: 4300, It: 4300, Total Error: 8.143e+02, Total Loss: 2.853e+00\n",
      "Epoch: 4300, It: 4300, Relative L2: 6.990e-04, Relative Linfty: 2.143e-03\n",
      "Epoch: 4300, It: 4300, Loss_helmholtz: 8.142e+02, Loss BCs(u): 3.727e-02\n",
      "Epoch: 4300, It: 4300, Lambda BCs: 6.377e+01, Lambda PDE: 3.662e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4350, It: 4350, Time: 1.24s, Learning Rate: 1.1e-03\n",
      "Epoch: 4350, It: 4350, Total Error: 8.050e+02, Total Loss: 2.821e+00\n",
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      "Epoch: 4350, It: 4350, Loss_helmholtz: 8.050e+02, Loss BCs(u): 3.685e-02\n",
      "Epoch: 4350, It: 4350, Lambda BCs: 6.377e+01, Lambda PDE: 3.691e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4400, It: 4400, Time: 1.24s, Learning Rate: 1.0e-03\n",
      "Epoch: 4400, It: 4400, Total Error: 7.960e+02, Total Loss: 2.789e+00\n",
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      "Epoch: 4400, It: 4400, Loss_helmholtz: 7.959e+02, Loss BCs(u): 3.644e-02\n",
      "Epoch: 4400, It: 4400, Lambda BCs: 6.377e+01, Lambda PDE: 3.708e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4450, It: 4450, Time: 1.24s, Learning Rate: 1.0e-03\n",
      "Epoch: 4450, It: 4450, Total Error: 7.871e+02, Total Loss: 2.759e+00\n",
      "Epoch: 4450, It: 4450, Relative L2: 1.102e-03, Relative Linfty: 5.553e-03\n",
      "Epoch: 4450, It: 4450, Loss_helmholtz: 7.871e+02, Loss BCs(u): 3.603e-02\n",
      "Epoch: 4450, It: 4450, Lambda BCs: 6.377e+01, Lambda PDE: 3.724e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4500, It: 4500, Time: 1.24s, Learning Rate: 1.0e-03\n",
      "Epoch: 4500, It: 4500, Total Error: 7.785e+02, Total Loss: 2.729e+00\n",
      "Epoch: 4500, It: 4500, Relative L2: 1.453e-03, Relative Linfty: 5.436e-03\n",
      "Epoch: 4500, It: 4500, Loss_helmholtz: 7.785e+02, Loss BCs(u): 3.563e-02\n",
      "Epoch: 4500, It: 4500, Lambda BCs: 6.377e+01, Lambda PDE: 3.747e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4550, It: 4550, Time: 1.24s, Learning Rate: 9.9e-04\n",
      "Epoch: 4550, It: 4550, Total Error: 7.700e+02, Total Loss: 2.700e+00\n",
      "Epoch: 4550, It: 4550, Relative L2: 6.515e-04, Relative Linfty: 2.054e-03\n",
      "Epoch: 4550, It: 4550, Loss_helmholtz: 7.700e+02, Loss BCs(u): 3.525e-02\n",
      "Epoch: 4550, It: 4550, Lambda BCs: 6.377e+01, Lambda PDE: 3.772e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4600, It: 4600, Time: 1.24s, Learning Rate: 9.7e-04\n",
      "Epoch: 4600, It: 4600, Total Error: 7.618e+02, Total Loss: 2.671e+00\n",
      "Epoch: 4600, It: 4600, Relative L2: 7.661e-04, Relative Linfty: 2.765e-03\n",
      "Epoch: 4600, It: 4600, Loss_helmholtz: 7.617e+02, Loss BCs(u): 3.487e-02\n",
      "Epoch: 4600, It: 4600, Lambda BCs: 6.377e+01, Lambda PDE: 3.802e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4650, It: 4650, Time: 1.24s, Learning Rate: 9.5e-04\n",
      "Epoch: 4650, It: 4650, Total Error: 7.536e+02, Total Loss: 2.643e+00\n",
      "Epoch: 4650, It: 4650, Relative L2: 9.987e-04, Relative Linfty: 3.210e-03\n",
      "Epoch: 4650, It: 4650, Loss_helmholtz: 7.536e+02, Loss BCs(u): 3.450e-02\n",
      "Epoch: 4650, It: 4650, Lambda BCs: 6.377e+01, Lambda PDE: 3.823e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4700, It: 4700, Time: 1.24s, Learning Rate: 9.4e-04\n",
      "Epoch: 4700, It: 4700, Total Error: 7.457e+02, Total Loss: 2.615e+00\n",
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      "Epoch: 4700, It: 4700, Loss_helmholtz: 7.457e+02, Loss BCs(u): 3.413e-02\n",
      "Epoch: 4700, It: 4700, Lambda BCs: 6.377e+01, Lambda PDE: 3.841e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4750, It: 4750, Time: 1.24s, Learning Rate: 9.2e-04\n",
      "Epoch: 4750, It: 4750, Total Error: 7.379e+02, Total Loss: 2.588e+00\n",
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      "Epoch: 4750, It: 4750, Loss_helmholtz: 7.379e+02, Loss BCs(u): 3.378e-02\n",
      "Epoch: 4750, It: 4750, Lambda BCs: 6.377e+01, Lambda PDE: 3.861e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4800, It: 4800, Time: 1.24s, Learning Rate: 9.0e-04\n",
      "Epoch: 4800, It: 4800, Total Error: 7.303e+02, Total Loss: 2.562e+00\n",
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      "Epoch: 4800, It: 4800, Loss_helmholtz: 7.303e+02, Loss BCs(u): 3.343e-02\n",
      "Epoch: 4800, It: 4800, Lambda BCs: 6.377e+01, Lambda PDE: 3.875e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4850, It: 4850, Time: 1.24s, Learning Rate: 8.9e-04\n",
      "Epoch: 4850, It: 4850, Total Error: 7.229e+02, Total Loss: 2.536e+00\n",
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      "Epoch: 4850, It: 4850, Loss_helmholtz: 7.229e+02, Loss BCs(u): 3.309e-02\n",
      "Epoch: 4850, It: 4850, Lambda BCs: 6.377e+01, Lambda PDE: 3.889e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4900, It: 4900, Time: 1.24s, Learning Rate: 8.7e-04\n",
      "Epoch: 4900, It: 4900, Total Error: 7.156e+02, Total Loss: 2.511e+00\n",
      "Epoch: 4900, It: 4900, Relative L2: 7.625e-04, Relative Linfty: 3.328e-03\n",
      "Epoch: 4900, It: 4900, Loss_helmholtz: 7.156e+02, Loss BCs(u): 3.276e-02\n",
      "Epoch: 4900, It: 4900, Lambda BCs: 6.377e+01, Lambda PDE: 3.910e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 4950, It: 4950, Time: 1.24s, Learning Rate: 8.6e-04\n",
      "Epoch: 4950, It: 4950, Total Error: 7.084e+02, Total Loss: 2.486e+00\n",
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      "Epoch: 4950, It: 4950, Lambda BCs: 6.377e+01, Lambda PDE: 3.927e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5000, It: 5000, Time: 1.25s, Learning Rate: 8.4e-04\n",
      "Epoch: 5000, It: 5000, Total Error: 7.014e+02, Total Loss: 2.462e+00\n",
      "Epoch: 5000, It: 5000, Relative L2: 6.203e-04, Relative Linfty: 2.799e-03\n",
      "Epoch: 5000, It: 5000, Loss_helmholtz: 7.014e+02, Loss BCs(u): 3.211e-02\n",
      "Epoch: 5000, It: 5000, Lambda BCs: 6.377e+01, Lambda PDE: 3.942e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5050, It: 5050, Time: 1.24s, Learning Rate: 8.3e-04\n",
      "Epoch: 5050, It: 5050, Total Error: 6.945e+02, Total Loss: 2.438e+00\n",
      "Epoch: 5050, It: 5050, Relative L2: 7.421e-04, Relative Linfty: 3.589e-03\n",
      "Epoch: 5050, It: 5050, Loss_helmholtz: 6.945e+02, Loss BCs(u): 3.179e-02\n",
      "Epoch: 5050, It: 5050, Lambda BCs: 6.377e+01, Lambda PDE: 3.953e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5100, It: 5100, Time: 1.25s, Learning Rate: 8.1e-04\n",
      "Epoch: 5100, It: 5100, Total Error: 6.878e+02, Total Loss: 2.414e+00\n",
      "Epoch: 5100, It: 5100, Relative L2: 8.873e-04, Relative Linfty: 2.667e-03\n",
      "Epoch: 5100, It: 5100, Loss_helmholtz: 6.878e+02, Loss BCs(u): 3.148e-02\n",
      "Epoch: 5100, It: 5100, Lambda BCs: 6.377e+01, Lambda PDE: 3.974e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5150, It: 5150, Time: 1.24s, Learning Rate: 8.0e-04\n",
      "Epoch: 5150, It: 5150, Total Error: 6.812e+02, Total Loss: 2.392e+00\n",
      "Epoch: 5150, It: 5150, Relative L2: 9.815e-04, Relative Linfty: 4.162e-03\n",
      "Epoch: 5150, It: 5150, Loss_helmholtz: 6.812e+02, Loss BCs(u): 3.118e-02\n",
      "Epoch: 5150, It: 5150, Lambda BCs: 6.377e+01, Lambda PDE: 3.991e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5200, It: 5200, Time: 1.24s, Learning Rate: 7.8e-04\n",
      "Epoch: 5200, It: 5200, Total Error: 6.747e+02, Total Loss: 2.369e+00\n",
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      "Epoch: 5200, It: 5200, Loss_helmholtz: 6.747e+02, Loss BCs(u): 3.088e-02\n",
      "Epoch: 5200, It: 5200, Lambda BCs: 6.377e+01, Lambda PDE: 4.009e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5250, It: 5250, Time: 1.24s, Learning Rate: 7.7e-04\n",
      "Epoch: 5250, It: 5250, Total Error: 6.683e+02, Total Loss: 2.347e+00\n",
      "Epoch: 5250, It: 5250, Relative L2: 8.489e-04, Relative Linfty: 3.421e-03\n",
      "Epoch: 5250, It: 5250, Loss_helmholtz: 6.683e+02, Loss BCs(u): 3.059e-02\n",
      "Epoch: 5250, It: 5250, Lambda BCs: 6.377e+01, Lambda PDE: 4.026e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5300, It: 5300, Time: 1.24s, Learning Rate: 7.6e-04\n",
      "Epoch: 5300, It: 5300, Total Error: 6.621e+02, Total Loss: 2.325e+00\n",
      "Epoch: 5300, It: 5300, Relative L2: 6.967e-04, Relative Linfty: 2.931e-03\n",
      "Epoch: 5300, It: 5300, Loss_helmholtz: 6.621e+02, Loss BCs(u): 3.031e-02\n",
      "Epoch: 5300, It: 5300, Lambda BCs: 6.377e+01, Lambda PDE: 4.044e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5350, It: 5350, Time: 1.24s, Learning Rate: 7.4e-04\n",
      "Epoch: 5350, It: 5350, Total Error: 6.560e+02, Total Loss: 2.304e+00\n",
      "Epoch: 5350, It: 5350, Relative L2: 5.519e-04, Relative Linfty: 1.753e-03\n",
      "Epoch: 5350, It: 5350, Loss_helmholtz: 6.559e+02, Loss BCs(u): 3.003e-02\n",
      "Epoch: 5350, It: 5350, Lambda BCs: 6.377e+01, Lambda PDE: 4.059e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5400, It: 5400, Time: 1.24s, Learning Rate: 7.3e-04\n",
      "Epoch: 5400, It: 5400, Total Error: 6.499e+02, Total Loss: 2.283e+00\n",
      "Epoch: 5400, It: 5400, Relative L2: 9.372e-04, Relative Linfty: 3.331e-03\n",
      "Epoch: 5400, It: 5400, Loss_helmholtz: 6.499e+02, Loss BCs(u): 2.975e-02\n",
      "Epoch: 5400, It: 5400, Lambda BCs: 6.377e+01, Lambda PDE: 4.079e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5450, It: 5450, Time: 1.24s, Learning Rate: 7.2e-04\n",
      "Epoch: 5450, It: 5450, Total Error: 6.440e+02, Total Loss: 2.262e+00\n",
      "Epoch: 5450, It: 5450, Relative L2: 6.395e-04, Relative Linfty: 2.188e-03\n",
      "Epoch: 5450, It: 5450, Loss_helmholtz: 6.440e+02, Loss BCs(u): 2.948e-02\n",
      "Epoch: 5450, It: 5450, Lambda BCs: 6.377e+01, Lambda PDE: 4.096e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5500, It: 5500, Time: 1.24s, Learning Rate: 7.0e-04\n",
      "Epoch: 5500, It: 5500, Total Error: 6.382e+02, Total Loss: 2.242e+00\n",
      "Epoch: 5500, It: 5500, Relative L2: 6.457e-04, Relative Linfty: 2.554e-03\n",
      "Epoch: 5500, It: 5500, Loss_helmholtz: 6.382e+02, Loss BCs(u): 2.921e-02\n",
      "Epoch: 5500, It: 5500, Lambda BCs: 6.377e+01, Lambda PDE: 4.113e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5550, It: 5550, Time: 1.24s, Learning Rate: 6.9e-04\n",
      "Epoch: 5550, It: 5550, Total Error: 6.325e+02, Total Loss: 2.222e+00\n",
      "Epoch: 5550, It: 5550, Relative L2: 6.683e-04, Relative Linfty: 2.717e-03\n",
      "Epoch: 5550, It: 5550, Loss_helmholtz: 6.325e+02, Loss BCs(u): 2.895e-02\n",
      "Epoch: 5550, It: 5550, Lambda BCs: 6.377e+01, Lambda PDE: 4.134e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5600, It: 5600, Time: 1.24s, Learning Rate: 6.8e-04\n",
      "Epoch: 5600, It: 5600, Total Error: 6.269e+02, Total Loss: 2.203e+00\n",
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      "Epoch: 5600, It: 5600, Loss_helmholtz: 6.269e+02, Loss BCs(u): 2.870e-02\n",
      "Epoch: 5600, It: 5600, Lambda BCs: 6.377e+01, Lambda PDE: 4.147e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5650, It: 5650, Time: 1.30s, Learning Rate: 6.7e-04\n",
      "Epoch: 5650, It: 5650, Total Error: 6.214e+02, Total Loss: 2.184e+00\n",
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      "Epoch: 5650, It: 5650, Loss_helmholtz: 6.214e+02, Loss BCs(u): 2.845e-02\n",
      "Epoch: 5650, It: 5650, Lambda BCs: 6.377e+01, Lambda PDE: 4.159e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5700, It: 5700, Time: 1.24s, Learning Rate: 6.5e-04\n",
      "Epoch: 5700, It: 5700, Total Error: 6.160e+02, Total Loss: 2.165e+00\n",
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      "Epoch: 5700, It: 5700, Loss_helmholtz: 6.160e+02, Loss BCs(u): 2.820e-02\n",
      "Epoch: 5700, It: 5700, Lambda BCs: 6.377e+01, Lambda PDE: 4.175e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5750, It: 5750, Time: 1.25s, Learning Rate: 6.4e-04\n",
      "Epoch: 5750, It: 5750, Total Error: 6.107e+02, Total Loss: 2.147e+00\n",
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      "Epoch: 5750, It: 5750, Loss_helmholtz: 6.107e+02, Loss BCs(u): 2.796e-02\n",
      "Epoch: 5750, It: 5750, Lambda BCs: 6.377e+01, Lambda PDE: 4.195e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5800, It: 5800, Time: 1.24s, Learning Rate: 6.3e-04\n",
      "Epoch: 5800, It: 5800, Total Error: 6.055e+02, Total Loss: 2.128e+00\n",
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      "Epoch: 5800, It: 5800, Loss_helmholtz: 6.055e+02, Loss BCs(u): 2.772e-02\n",
      "Epoch: 5800, It: 5800, Lambda BCs: 6.377e+01, Lambda PDE: 4.209e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5850, It: 5850, Time: 1.24s, Learning Rate: 6.2e-04\n",
      "Epoch: 5850, It: 5850, Total Error: 6.004e+02, Total Loss: 2.111e+00\n",
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      "Epoch: 5850, It: 5850, Loss_helmholtz: 6.003e+02, Loss BCs(u): 2.748e-02\n",
      "Epoch: 5850, It: 5850, Lambda BCs: 6.377e+01, Lambda PDE: 4.225e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5900, It: 5900, Time: 1.24s, Learning Rate: 6.1e-04\n",
      "Epoch: 5900, It: 5900, Total Error: 5.953e+02, Total Loss: 2.093e+00\n",
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      "Epoch: 5900, It: 5900, Loss_helmholtz: 5.953e+02, Loss BCs(u): 2.725e-02\n",
      "Epoch: 5900, It: 5900, Lambda BCs: 6.377e+01, Lambda PDE: 4.239e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 5950, It: 5950, Time: 1.24s, Learning Rate: 6.0e-04\n",
      "Epoch: 5950, It: 5950, Total Error: 5.904e+02, Total Loss: 2.076e+00\n",
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      "Epoch: 5950, It: 5950, Loss_helmholtz: 5.903e+02, Loss BCs(u): 2.702e-02\n",
      "Epoch: 5950, It: 5950, Lambda BCs: 6.377e+01, Lambda PDE: 4.255e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6000, It: 6000, Time: 1.24s, Learning Rate: 5.9e-04\n",
      "Epoch: 6000, It: 6000, Total Error: 5.855e+02, Total Loss: 2.059e+00\n",
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      "Epoch: 6000, It: 6000, Lambda BCs: 6.377e+01, Lambda PDE: 4.269e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6050, It: 6050, Time: 1.32s, Learning Rate: 5.8e-04\n",
      "Epoch: 6050, It: 6050, Total Error: 5.807e+02, Total Loss: 2.042e+00\n",
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      "Epoch: 6050, It: 6050, Loss_helmholtz: 5.807e+02, Loss BCs(u): 2.658e-02\n",
      "Epoch: 6050, It: 6050, Lambda BCs: 6.377e+01, Lambda PDE: 4.281e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6100, It: 6100, Time: 1.24s, Learning Rate: 5.7e-04\n",
      "Epoch: 6100, It: 6100, Total Error: 5.760e+02, Total Loss: 2.026e+00\n",
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      "Epoch: 6100, It: 6100, Loss_helmholtz: 5.759e+02, Loss BCs(u): 2.636e-02\n",
      "Epoch: 6100, It: 6100, Lambda BCs: 6.377e+01, Lambda PDE: 4.295e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6150, It: 6150, Time: 1.24s, Learning Rate: 5.6e-04\n",
      "Epoch: 6150, It: 6150, Total Error: 5.713e+02, Total Loss: 2.009e+00\n",
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      "Epoch: 6150, It: 6150, Loss_helmholtz: 5.713e+02, Loss BCs(u): 2.615e-02\n",
      "Epoch: 6150, It: 6150, Lambda BCs: 6.377e+01, Lambda PDE: 4.309e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6200, It: 6200, Time: 1.24s, Learning Rate: 5.5e-04\n",
      "Epoch: 6200, It: 6200, Total Error: 5.668e+02, Total Loss: 1.993e+00\n",
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      "Epoch: 6200, It: 6200, Lambda BCs: 6.377e+01, Lambda PDE: 4.324e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6250, It: 6250, Time: 1.24s, Learning Rate: 5.4e-04\n",
      "Epoch: 6250, It: 6250, Total Error: 5.623e+02, Total Loss: 1.978e+00\n",
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      "Epoch: 6250, It: 6250, Loss_helmholtz: 5.622e+02, Loss BCs(u): 2.574e-02\n",
      "Epoch: 6250, It: 6250, Lambda BCs: 6.377e+01, Lambda PDE: 4.339e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6300, It: 6300, Time: 1.24s, Learning Rate: 5.3e-04\n",
      "Epoch: 6300, It: 6300, Total Error: 5.578e+02, Total Loss: 1.962e+00\n",
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      "Epoch: 6300, It: 6300, Loss_helmholtz: 5.578e+02, Loss BCs(u): 2.553e-02\n",
      "Epoch: 6300, It: 6300, Lambda BCs: 6.377e+01, Lambda PDE: 4.348e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6350, It: 6350, Time: 1.24s, Learning Rate: 5.2e-04\n",
      "Epoch: 6350, It: 6350, Total Error: 5.535e+02, Total Loss: 1.947e+00\n",
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      "Epoch: 6350, It: 6350, Loss_helmholtz: 5.534e+02, Loss BCs(u): 2.533e-02\n",
      "Epoch: 6350, It: 6350, Lambda BCs: 6.377e+01, Lambda PDE: 4.357e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6400, It: 6400, Time: 1.24s, Learning Rate: 5.1e-04\n",
      "Epoch: 6400, It: 6400, Total Error: 5.492e+02, Total Loss: 1.932e+00\n",
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      "Epoch: 6400, It: 6400, Loss_helmholtz: 5.492e+02, Loss BCs(u): 2.514e-02\n",
      "Epoch: 6400, It: 6400, Lambda BCs: 6.377e+01, Lambda PDE: 4.375e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6450, It: 6450, Time: 1.24s, Learning Rate: 5.0e-04\n",
      "Epoch: 6450, It: 6450, Total Error: 5.450e+02, Total Loss: 1.917e+00\n",
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      "Epoch: 6450, It: 6450, Loss_helmholtz: 5.449e+02, Loss BCs(u): 2.494e-02\n",
      "Epoch: 6450, It: 6450, Lambda BCs: 6.377e+01, Lambda PDE: 4.390e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6500, It: 6500, Time: 1.24s, Learning Rate: 4.9e-04\n",
      "Epoch: 6500, It: 6500, Total Error: 5.408e+02, Total Loss: 1.903e+00\n",
      "Epoch: 6500, It: 6500, Relative L2: 5.315e-04, Relative Linfty: 2.483e-03\n",
      "Epoch: 6500, It: 6500, Loss_helmholtz: 5.408e+02, Loss BCs(u): 2.475e-02\n",
      "Epoch: 6500, It: 6500, Lambda BCs: 6.377e+01, Lambda PDE: 4.402e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6550, It: 6550, Time: 1.24s, Learning Rate: 4.8e-04\n",
      "Epoch: 6550, It: 6550, Total Error: 5.367e+02, Total Loss: 1.889e+00\n",
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      "Epoch: 6550, It: 6550, Loss_helmholtz: 5.367e+02, Loss BCs(u): 2.457e-02\n",
      "Epoch: 6550, It: 6550, Lambda BCs: 6.377e+01, Lambda PDE: 4.412e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6600, It: 6600, Time: 1.24s, Learning Rate: 4.7e-04\n",
      "Epoch: 6600, It: 6600, Total Error: 5.327e+02, Total Loss: 1.875e+00\n",
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      "Epoch: 6600, It: 6600, Loss_helmholtz: 5.326e+02, Loss BCs(u): 2.438e-02\n",
      "Epoch: 6600, It: 6600, Lambda BCs: 6.377e+01, Lambda PDE: 4.426e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6650, It: 6650, Time: 1.24s, Learning Rate: 4.7e-04\n",
      "Epoch: 6650, It: 6650, Total Error: 5.287e+02, Total Loss: 1.861e+00\n",
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      "Epoch: 6650, It: 6650, Loss_helmholtz: 5.287e+02, Loss BCs(u): 2.420e-02\n",
      "Epoch: 6650, It: 6650, Lambda BCs: 6.377e+01, Lambda PDE: 4.441e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6700, It: 6700, Time: 1.25s, Learning Rate: 4.6e-04\n",
      "Epoch: 6700, It: 6700, Total Error: 5.248e+02, Total Loss: 1.847e+00\n",
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      "Epoch: 6700, It: 6700, Loss_helmholtz: 5.247e+02, Loss BCs(u): 2.402e-02\n",
      "Epoch: 6700, It: 6700, Lambda BCs: 6.377e+01, Lambda PDE: 4.447e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6750, It: 6750, Time: 1.24s, Learning Rate: 4.5e-04\n",
      "Epoch: 6750, It: 6750, Total Error: 5.209e+02, Total Loss: 1.833e+00\n",
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      "Epoch: 6750, It: 6750, Loss_helmholtz: 5.209e+02, Loss BCs(u): 2.384e-02\n",
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      "--------------------------------------------------\n",
      "Epoch: 6800, It: 6800, Time: 1.24s, Learning Rate: 4.4e-04\n",
      "Epoch: 6800, It: 6800, Total Error: 5.171e+02, Total Loss: 1.820e+00\n",
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      "Epoch: 6800, It: 6800, Loss_helmholtz: 5.171e+02, Loss BCs(u): 2.367e-02\n",
      "Epoch: 6800, It: 6800, Lambda BCs: 6.377e+01, Lambda PDE: 4.471e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6850, It: 6850, Time: 1.24s, Learning Rate: 4.3e-04\n",
      "Epoch: 6850, It: 6850, Total Error: 5.134e+02, Total Loss: 1.807e+00\n",
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      "Epoch: 6850, It: 6850, Lambda BCs: 6.377e+01, Lambda PDE: 4.490e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6900, It: 6900, Time: 1.24s, Learning Rate: 4.3e-04\n",
      "Epoch: 6900, It: 6900, Total Error: 5.097e+02, Total Loss: 1.794e+00\n",
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      "Epoch: 6900, It: 6900, Lambda BCs: 6.377e+01, Lambda PDE: 4.502e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 6950, It: 6950, Time: 1.24s, Learning Rate: 4.2e-04\n",
      "Epoch: 6950, It: 6950, Total Error: 5.060e+02, Total Loss: 1.781e+00\n",
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      "Epoch: 6950, It: 6950, Lambda BCs: 6.377e+01, Lambda PDE: 4.518e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7000, It: 7000, Time: 1.24s, Learning Rate: 4.1e-04\n",
      "Epoch: 7000, It: 7000, Total Error: 5.024e+02, Total Loss: 1.769e+00\n",
      "Epoch: 7000, It: 7000, Relative L2: 3.716e-04, Relative Linfty: 1.340e-03\n",
      "Epoch: 7000, It: 7000, Loss_helmholtz: 5.024e+02, Loss BCs(u): 2.300e-02\n",
      "Epoch: 7000, It: 7000, Lambda BCs: 6.377e+01, Lambda PDE: 4.531e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7050, It: 7050, Time: 1.24s, Learning Rate: 4.0e-04\n",
      "Epoch: 7050, It: 7050, Total Error: 4.989e+02, Total Loss: 1.756e+00\n",
      "Epoch: 7050, It: 7050, Relative L2: 4.837e-04, Relative Linfty: 2.392e-03\n",
      "Epoch: 7050, It: 7050, Loss_helmholtz: 4.989e+02, Loss BCs(u): 2.284e-02\n",
      "Epoch: 7050, It: 7050, Lambda BCs: 6.377e+01, Lambda PDE: 4.547e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7100, It: 7100, Time: 1.24s, Learning Rate: 4.0e-04\n",
      "Epoch: 7100, It: 7100, Total Error: 4.954e+02, Total Loss: 1.744e+00\n",
      "Epoch: 7100, It: 7100, Relative L2: 3.322e-04, Relative Linfty: 1.368e-03\n",
      "Epoch: 7100, It: 7100, Loss_helmholtz: 4.954e+02, Loss BCs(u): 2.268e-02\n",
      "Epoch: 7100, It: 7100, Lambda BCs: 6.377e+01, Lambda PDE: 4.557e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7150, It: 7150, Time: 1.24s, Learning Rate: 3.9e-04\n",
      "Epoch: 7150, It: 7150, Total Error: 4.920e+02, Total Loss: 1.732e+00\n",
      "Epoch: 7150, It: 7150, Relative L2: 4.526e-04, Relative Linfty: 1.543e-03\n",
      "Epoch: 7150, It: 7150, Loss_helmholtz: 4.920e+02, Loss BCs(u): 2.252e-02\n",
      "Epoch: 7150, It: 7150, Lambda BCs: 6.377e+01, Lambda PDE: 4.570e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7200, It: 7200, Time: 1.24s, Learning Rate: 3.8e-04\n",
      "Epoch: 7200, It: 7200, Total Error: 4.886e+02, Total Loss: 1.720e+00\n",
      "Epoch: 7200, It: 7200, Relative L2: 3.762e-04, Relative Linfty: 1.390e-03\n",
      "Epoch: 7200, It: 7200, Loss_helmholtz: 4.886e+02, Loss BCs(u): 2.236e-02\n",
      "Epoch: 7200, It: 7200, Lambda BCs: 6.377e+01, Lambda PDE: 4.588e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7250, It: 7250, Time: 1.24s, Learning Rate: 3.8e-04\n",
      "Epoch: 7250, It: 7250, Total Error: 4.852e+02, Total Loss: 1.708e+00\n",
      "Epoch: 7250, It: 7250, Relative L2: 3.672e-04, Relative Linfty: 1.360e-03\n",
      "Epoch: 7250, It: 7250, Loss_helmholtz: 4.852e+02, Loss BCs(u): 2.221e-02\n",
      "Epoch: 7250, It: 7250, Lambda BCs: 6.377e+01, Lambda PDE: 4.605e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7300, It: 7300, Time: 1.24s, Learning Rate: 3.7e-04\n",
      "Epoch: 7300, It: 7300, Total Error: 4.819e+02, Total Loss: 1.697e+00\n",
      "Epoch: 7300, It: 7300, Relative L2: 4.010e-04, Relative Linfty: 2.018e-03\n",
      "Epoch: 7300, It: 7300, Loss_helmholtz: 4.819e+02, Loss BCs(u): 2.206e-02\n",
      "Epoch: 7300, It: 7300, Lambda BCs: 6.377e+01, Lambda PDE: 4.619e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7350, It: 7350, Time: 1.24s, Learning Rate: 3.6e-04\n",
      "Epoch: 7350, It: 7350, Total Error: 4.787e+02, Total Loss: 1.685e+00\n",
      "Epoch: 7350, It: 7350, Relative L2: 4.127e-04, Relative Linfty: 1.830e-03\n",
      "Epoch: 7350, It: 7350, Loss_helmholtz: 4.787e+02, Loss BCs(u): 2.191e-02\n",
      "Epoch: 7350, It: 7350, Lambda BCs: 6.377e+01, Lambda PDE: 4.631e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7400, It: 7400, Time: 1.24s, Learning Rate: 3.6e-04\n",
      "Epoch: 7400, It: 7400, Total Error: 4.755e+02, Total Loss: 1.674e+00\n",
      "Epoch: 7400, It: 7400, Relative L2: 3.330e-04, Relative Linfty: 1.609e-03\n",
      "Epoch: 7400, It: 7400, Loss_helmholtz: 4.754e+02, Loss BCs(u): 2.176e-02\n",
      "Epoch: 7400, It: 7400, Lambda BCs: 6.377e+01, Lambda PDE: 4.643e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7450, It: 7450, Time: 1.25s, Learning Rate: 3.5e-04\n",
      "Epoch: 7450, It: 7450, Total Error: 4.723e+02, Total Loss: 1.663e+00\n",
      "Epoch: 7450, It: 7450, Relative L2: 4.714e-04, Relative Linfty: 1.733e-03\n",
      "Epoch: 7450, It: 7450, Loss_helmholtz: 4.723e+02, Loss BCs(u): 2.162e-02\n",
      "Epoch: 7450, It: 7450, Lambda BCs: 6.377e+01, Lambda PDE: 4.655e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7500, It: 7500, Time: 1.24s, Learning Rate: 3.4e-04\n",
      "Epoch: 7500, It: 7500, Total Error: 4.692e+02, Total Loss: 1.652e+00\n",
      "Epoch: 7500, It: 7500, Relative L2: 4.076e-04, Relative Linfty: 1.636e-03\n",
      "Epoch: 7500, It: 7500, Loss_helmholtz: 4.691e+02, Loss BCs(u): 2.148e-02\n",
      "Epoch: 7500, It: 7500, Lambda BCs: 6.377e+01, Lambda PDE: 4.669e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7550, It: 7550, Time: 1.24s, Learning Rate: 3.4e-04\n",
      "Epoch: 7550, It: 7550, Total Error: 4.661e+02, Total Loss: 1.641e+00\n",
      "Epoch: 7550, It: 7550, Relative L2: 3.835e-04, Relative Linfty: 1.765e-03\n",
      "Epoch: 7550, It: 7550, Loss_helmholtz: 4.661e+02, Loss BCs(u): 2.133e-02\n",
      "Epoch: 7550, It: 7550, Lambda BCs: 6.377e+01, Lambda PDE: 4.683e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7600, It: 7600, Time: 1.24s, Learning Rate: 3.3e-04\n",
      "Epoch: 7600, It: 7600, Total Error: 4.630e+02, Total Loss: 1.631e+00\n",
      "Epoch: 7600, It: 7600, Relative L2: 3.519e-04, Relative Linfty: 1.516e-03\n",
      "Epoch: 7600, It: 7600, Loss_helmholtz: 4.630e+02, Loss BCs(u): 2.119e-02\n",
      "Epoch: 7600, It: 7600, Lambda BCs: 6.377e+01, Lambda PDE: 4.700e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7650, It: 7650, Time: 1.24s, Learning Rate: 3.3e-04\n",
      "Epoch: 7650, It: 7650, Total Error: 4.600e+02, Total Loss: 1.620e+00\n",
      "Epoch: 7650, It: 7650, Relative L2: 4.128e-04, Relative Linfty: 1.549e-03\n",
      "Epoch: 7650, It: 7650, Loss_helmholtz: 4.600e+02, Loss BCs(u): 2.106e-02\n",
      "Epoch: 7650, It: 7650, Lambda BCs: 6.377e+01, Lambda PDE: 4.712e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7700, It: 7700, Time: 1.24s, Learning Rate: 3.2e-04\n",
      "Epoch: 7700, It: 7700, Total Error: 4.571e+02, Total Loss: 1.610e+00\n",
      "Epoch: 7700, It: 7700, Relative L2: 3.163e-04, Relative Linfty: 1.455e-03\n",
      "Epoch: 7700, It: 7700, Loss_helmholtz: 4.570e+02, Loss BCs(u): 2.092e-02\n",
      "Epoch: 7700, It: 7700, Lambda BCs: 6.377e+01, Lambda PDE: 4.723e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7750, It: 7750, Time: 1.24s, Learning Rate: 3.2e-04\n",
      "Epoch: 7750, It: 7750, Total Error: 4.541e+02, Total Loss: 1.599e+00\n",
      "Epoch: 7750, It: 7750, Relative L2: 3.103e-04, Relative Linfty: 1.305e-03\n",
      "Epoch: 7750, It: 7750, Loss_helmholtz: 4.541e+02, Loss BCs(u): 2.079e-02\n",
      "Epoch: 7750, It: 7750, Lambda BCs: 6.377e+01, Lambda PDE: 4.737e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7800, It: 7800, Time: 1.24s, Learning Rate: 3.1e-04\n",
      "Epoch: 7800, It: 7800, Total Error: 4.512e+02, Total Loss: 1.589e+00\n",
      "Epoch: 7800, It: 7800, Relative L2: 2.898e-04, Relative Linfty: 1.125e-03\n",
      "Epoch: 7800, It: 7800, Loss_helmholtz: 4.512e+02, Loss BCs(u): 2.065e-02\n",
      "Epoch: 7800, It: 7800, Lambda BCs: 6.377e+01, Lambda PDE: 4.755e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7850, It: 7850, Time: 1.24s, Learning Rate: 3.0e-04\n",
      "Epoch: 7850, It: 7850, Total Error: 4.484e+02, Total Loss: 1.579e+00\n",
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      "Epoch: 7850, It: 7850, Loss_helmholtz: 4.484e+02, Loss BCs(u): 2.052e-02\n",
      "Epoch: 7850, It: 7850, Lambda BCs: 6.377e+01, Lambda PDE: 4.770e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7900, It: 7900, Time: 1.24s, Learning Rate: 3.0e-04\n",
      "Epoch: 7900, It: 7900, Total Error: 4.456e+02, Total Loss: 1.569e+00\n",
      "Epoch: 7900, It: 7900, Relative L2: 2.718e-04, Relative Linfty: 9.939e-04\n",
      "Epoch: 7900, It: 7900, Loss_helmholtz: 4.455e+02, Loss BCs(u): 2.039e-02\n",
      "Epoch: 7900, It: 7900, Lambda BCs: 6.377e+01, Lambda PDE: 4.787e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 7950, It: 7950, Time: 1.24s, Learning Rate: 2.9e-04\n",
      "Epoch: 7950, It: 7950, Total Error: 4.428e+02, Total Loss: 1.560e+00\n",
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      "Epoch: 7950, It: 7950, Loss_helmholtz: 4.428e+02, Loss BCs(u): 2.027e-02\n",
      "Epoch: 7950, It: 7950, Lambda BCs: 6.377e+01, Lambda PDE: 4.798e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8000, It: 8000, Time: 1.24s, Learning Rate: 2.9e-04\n",
      "Epoch: 8000, It: 8000, Total Error: 4.400e+02, Total Loss: 1.550e+00\n",
      "Epoch: 8000, It: 8000, Relative L2: 3.341e-04, Relative Linfty: 1.426e-03\n",
      "Epoch: 8000, It: 8000, Loss_helmholtz: 4.400e+02, Loss BCs(u): 2.014e-02\n",
      "Epoch: 8000, It: 8000, Lambda BCs: 6.377e+01, Lambda PDE: 4.817e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8050, It: 8050, Time: 1.26s, Learning Rate: 2.8e-04\n",
      "Epoch: 8050, It: 8050, Total Error: 4.373e+02, Total Loss: 1.540e+00\n",
      "Epoch: 8050, It: 8050, Relative L2: 2.757e-04, Relative Linfty: 1.008e-03\n",
      "Epoch: 8050, It: 8050, Loss_helmholtz: 4.373e+02, Loss BCs(u): 2.002e-02\n",
      "Epoch: 8050, It: 8050, Lambda BCs: 6.377e+01, Lambda PDE: 4.830e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8100, It: 8100, Time: 1.24s, Learning Rate: 2.8e-04\n",
      "Epoch: 8100, It: 8100, Total Error: 4.346e+02, Total Loss: 1.531e+00\n",
      "Epoch: 8100, It: 8100, Relative L2: 3.366e-04, Relative Linfty: 1.206e-03\n",
      "Epoch: 8100, It: 8100, Loss_helmholtz: 4.346e+02, Loss BCs(u): 1.989e-02\n",
      "Epoch: 8100, It: 8100, Lambda BCs: 6.377e+01, Lambda PDE: 4.844e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8150, It: 8150, Time: 1.24s, Learning Rate: 2.7e-04\n",
      "Epoch: 8150, It: 8150, Total Error: 4.320e+02, Total Loss: 1.522e+00\n",
      "Epoch: 8150, It: 8150, Relative L2: 3.085e-04, Relative Linfty: 1.522e-03\n",
      "Epoch: 8150, It: 8150, Loss_helmholtz: 4.320e+02, Loss BCs(u): 1.977e-02\n",
      "Epoch: 8150, It: 8150, Lambda BCs: 6.377e+01, Lambda PDE: 4.865e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8200, It: 8200, Time: 1.24s, Learning Rate: 2.7e-04\n",
      "Epoch: 8200, It: 8200, Total Error: 4.294e+02, Total Loss: 1.513e+00\n",
      "Epoch: 8200, It: 8200, Relative L2: 3.743e-04, Relative Linfty: 1.420e-03\n",
      "Epoch: 8200, It: 8200, Loss_helmholtz: 4.293e+02, Loss BCs(u): 1.965e-02\n",
      "Epoch: 8200, It: 8200, Lambda BCs: 6.377e+01, Lambda PDE: 4.875e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8250, It: 8250, Time: 1.24s, Learning Rate: 2.6e-04\n",
      "Epoch: 8250, It: 8250, Total Error: 4.268e+02, Total Loss: 1.503e+00\n",
      "Epoch: 8250, It: 8250, Relative L2: 2.865e-04, Relative Linfty: 1.168e-03\n",
      "Epoch: 8250, It: 8250, Loss_helmholtz: 4.268e+02, Loss BCs(u): 1.953e-02\n",
      "Epoch: 8250, It: 8250, Lambda BCs: 6.377e+01, Lambda PDE: 4.890e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8300, It: 8300, Time: 1.24s, Learning Rate: 2.6e-04\n",
      "Epoch: 8300, It: 8300, Total Error: 4.242e+02, Total Loss: 1.494e+00\n",
      "Epoch: 8300, It: 8300, Relative L2: 2.796e-04, Relative Linfty: 1.248e-03\n",
      "Epoch: 8300, It: 8300, Loss_helmholtz: 4.242e+02, Loss BCs(u): 1.942e-02\n",
      "Epoch: 8300, It: 8300, Lambda BCs: 6.377e+01, Lambda PDE: 4.908e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8350, It: 8350, Time: 1.24s, Learning Rate: 2.5e-04\n",
      "Epoch: 8350, It: 8350, Total Error: 4.217e+02, Total Loss: 1.486e+00\n",
      "Epoch: 8350, It: 8350, Relative L2: 3.639e-04, Relative Linfty: 1.617e-03\n",
      "Epoch: 8350, It: 8350, Loss_helmholtz: 4.217e+02, Loss BCs(u): 1.930e-02\n",
      "Epoch: 8350, It: 8350, Lambda BCs: 6.377e+01, Lambda PDE: 4.922e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8400, It: 8400, Time: 1.24s, Learning Rate: 2.5e-04\n",
      "Epoch: 8400, It: 8400, Total Error: 4.192e+02, Total Loss: 1.477e+00\n",
      "Epoch: 8400, It: 8400, Relative L2: 2.943e-04, Relative Linfty: 1.454e-03\n",
      "Epoch: 8400, It: 8400, Loss_helmholtz: 4.192e+02, Loss BCs(u): 1.919e-02\n",
      "Epoch: 8400, It: 8400, Lambda BCs: 6.377e+01, Lambda PDE: 4.937e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8450, It: 8450, Time: 1.24s, Learning Rate: 2.5e-04\n",
      "Epoch: 8450, It: 8450, Total Error: 4.167e+02, Total Loss: 1.468e+00\n",
      "Epoch: 8450, It: 8450, Relative L2: 2.764e-04, Relative Linfty: 1.068e-03\n",
      "Epoch: 8450, It: 8450, Loss_helmholtz: 4.167e+02, Loss BCs(u): 1.908e-02\n",
      "Epoch: 8450, It: 8450, Lambda BCs: 6.377e+01, Lambda PDE: 4.956e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8500, It: 8500, Time: 1.24s, Learning Rate: 2.4e-04\n",
      "Epoch: 8500, It: 8500, Total Error: 4.143e+02, Total Loss: 1.460e+00\n",
      "Epoch: 8500, It: 8500, Relative L2: 2.665e-04, Relative Linfty: 1.299e-03\n",
      "Epoch: 8500, It: 8500, Loss_helmholtz: 4.143e+02, Loss BCs(u): 1.896e-02\n",
      "Epoch: 8500, It: 8500, Lambda BCs: 6.377e+01, Lambda PDE: 4.972e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8550, It: 8550, Time: 1.24s, Learning Rate: 2.4e-04\n",
      "Epoch: 8550, It: 8550, Total Error: 4.119e+02, Total Loss: 1.451e+00\n",
      "Epoch: 8550, It: 8550, Relative L2: 2.362e-04, Relative Linfty: 8.595e-04\n",
      "Epoch: 8550, It: 8550, Loss_helmholtz: 4.119e+02, Loss BCs(u): 1.885e-02\n",
      "Epoch: 8550, It: 8550, Lambda BCs: 6.377e+01, Lambda PDE: 4.992e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8600, It: 8600, Time: 1.24s, Learning Rate: 2.3e-04\n",
      "Epoch: 8600, It: 8600, Total Error: 4.095e+02, Total Loss: 1.443e+00\n",
      "Epoch: 8600, It: 8600, Relative L2: 3.519e-04, Relative Linfty: 1.706e-03\n",
      "Epoch: 8600, It: 8600, Loss_helmholtz: 4.095e+02, Loss BCs(u): 1.874e-02\n",
      "Epoch: 8600, It: 8600, Lambda BCs: 6.377e+01, Lambda PDE: 5.009e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8650, It: 8650, Time: 1.24s, Learning Rate: 2.3e-04\n",
      "Epoch: 8650, It: 8650, Total Error: 4.072e+02, Total Loss: 1.435e+00\n",
      "Epoch: 8650, It: 8650, Relative L2: 2.339e-04, Relative Linfty: 9.898e-04\n",
      "Epoch: 8650, It: 8650, Loss_helmholtz: 4.071e+02, Loss BCs(u): 1.864e-02\n",
      "Epoch: 8650, It: 8650, Lambda BCs: 6.377e+01, Lambda PDE: 5.029e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8700, It: 8700, Time: 1.24s, Learning Rate: 2.2e-04\n",
      "Epoch: 8700, It: 8700, Total Error: 4.048e+02, Total Loss: 1.426e+00\n",
      "Epoch: 8700, It: 8700, Relative L2: 2.861e-04, Relative Linfty: 1.415e-03\n",
      "Epoch: 8700, It: 8700, Loss_helmholtz: 4.048e+02, Loss BCs(u): 1.853e-02\n",
      "Epoch: 8700, It: 8700, Lambda BCs: 6.377e+01, Lambda PDE: 5.040e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8750, It: 8750, Time: 1.24s, Learning Rate: 2.2e-04\n",
      "Epoch: 8750, It: 8750, Total Error: 4.025e+02, Total Loss: 1.418e+00\n",
      "Epoch: 8750, It: 8750, Relative L2: 2.779e-04, Relative Linfty: 1.241e-03\n",
      "Epoch: 8750, It: 8750, Loss_helmholtz: 4.025e+02, Loss BCs(u): 1.842e-02\n",
      "Epoch: 8750, It: 8750, Lambda BCs: 6.377e+01, Lambda PDE: 5.053e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8800, It: 8800, Time: 1.24s, Learning Rate: 2.2e-04\n",
      "Epoch: 8800, It: 8800, Total Error: 4.003e+02, Total Loss: 1.410e+00\n",
      "Epoch: 8800, It: 8800, Relative L2: 2.452e-04, Relative Linfty: 1.172e-03\n",
      "Epoch: 8800, It: 8800, Loss_helmholtz: 4.002e+02, Loss BCs(u): 1.832e-02\n",
      "Epoch: 8800, It: 8800, Lambda BCs: 6.377e+01, Lambda PDE: 5.067e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8850, It: 8850, Time: 1.24s, Learning Rate: 2.1e-04\n",
      "Epoch: 8850, It: 8850, Total Error: 3.980e+02, Total Loss: 1.402e+00\n",
      "Epoch: 8850, It: 8850, Relative L2: 2.085e-04, Relative Linfty: 6.541e-04\n",
      "Epoch: 8850, It: 8850, Loss_helmholtz: 3.980e+02, Loss BCs(u): 1.822e-02\n",
      "Epoch: 8850, It: 8850, Lambda BCs: 6.377e+01, Lambda PDE: 5.080e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8900, It: 8900, Time: 1.24s, Learning Rate: 2.1e-04\n",
      "Epoch: 8900, It: 8900, Total Error: 3.958e+02, Total Loss: 1.395e+00\n",
      "Epoch: 8900, It: 8900, Relative L2: 2.530e-04, Relative Linfty: 1.086e-03\n",
      "Epoch: 8900, It: 8900, Loss_helmholtz: 3.958e+02, Loss BCs(u): 1.812e-02\n",
      "Epoch: 8900, It: 8900, Lambda BCs: 6.377e+01, Lambda PDE: 5.099e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 8950, It: 8950, Time: 1.24s, Learning Rate: 2.1e-04\n",
      "Epoch: 8950, It: 8950, Total Error: 3.936e+02, Total Loss: 1.387e+00\n",
      "Epoch: 8950, It: 8950, Relative L2: 2.893e-04, Relative Linfty: 9.643e-04\n",
      "Epoch: 8950, It: 8950, Loss_helmholtz: 3.936e+02, Loss BCs(u): 1.802e-02\n",
      "Epoch: 8950, It: 8950, Lambda BCs: 6.377e+01, Lambda PDE: 5.118e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9000, It: 9000, Time: 1.24s, Learning Rate: 2.0e-04\n",
      "Epoch: 9000, It: 9000, Total Error: 3.914e+02, Total Loss: 1.379e+00\n",
      "Epoch: 9000, It: 9000, Relative L2: 2.178e-04, Relative Linfty: 6.725e-04\n",
      "Epoch: 9000, It: 9000, Loss_helmholtz: 3.914e+02, Loss BCs(u): 1.792e-02\n",
      "Epoch: 9000, It: 9000, Lambda BCs: 6.377e+01, Lambda PDE: 5.136e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9050, It: 9050, Time: 1.24s, Learning Rate: 2.0e-04\n",
      "Epoch: 9050, It: 9050, Total Error: 3.893e+02, Total Loss: 1.372e+00\n",
      "Epoch: 9050, It: 9050, Relative L2: 3.411e-04, Relative Linfty: 1.219e-03\n",
      "Epoch: 9050, It: 9050, Loss_helmholtz: 3.892e+02, Loss BCs(u): 1.782e-02\n",
      "Epoch: 9050, It: 9050, Lambda BCs: 6.377e+01, Lambda PDE: 5.158e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9100, It: 9100, Time: 1.24s, Learning Rate: 1.9e-04\n",
      "Epoch: 9100, It: 9100, Total Error: 3.871e+02, Total Loss: 1.364e+00\n",
      "Epoch: 9100, It: 9100, Relative L2: 2.614e-04, Relative Linfty: 1.105e-03\n",
      "Epoch: 9100, It: 9100, Loss_helmholtz: 3.871e+02, Loss BCs(u): 1.772e-02\n",
      "Epoch: 9100, It: 9100, Lambda BCs: 6.377e+01, Lambda PDE: 5.178e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9150, It: 9150, Time: 1.24s, Learning Rate: 1.9e-04\n",
      "Epoch: 9150, It: 9150, Total Error: 3.850e+02, Total Loss: 1.357e+00\n",
      "Epoch: 9150, It: 9150, Relative L2: 2.451e-04, Relative Linfty: 9.956e-04\n",
      "Epoch: 9150, It: 9150, Loss_helmholtz: 3.850e+02, Loss BCs(u): 1.762e-02\n",
      "Epoch: 9150, It: 9150, Lambda BCs: 6.377e+01, Lambda PDE: 5.197e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9200, It: 9200, Time: 1.24s, Learning Rate: 1.9e-04\n",
      "Epoch: 9200, It: 9200, Total Error: 3.829e+02, Total Loss: 1.349e+00\n",
      "Epoch: 9200, It: 9200, Relative L2: 3.007e-04, Relative Linfty: 9.456e-04\n",
      "Epoch: 9200, It: 9200, Loss_helmholtz: 3.829e+02, Loss BCs(u): 1.753e-02\n",
      "Epoch: 9200, It: 9200, Lambda BCs: 6.377e+01, Lambda PDE: 5.217e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9250, It: 9250, Time: 1.24s, Learning Rate: 1.8e-04\n",
      "Epoch: 9250, It: 9250, Total Error: 3.809e+02, Total Loss: 1.342e+00\n",
      "Epoch: 9250, It: 9250, Relative L2: 2.521e-04, Relative Linfty: 7.918e-04\n",
      "Epoch: 9250, It: 9250, Loss_helmholtz: 3.809e+02, Loss BCs(u): 1.743e-02\n",
      "Epoch: 9250, It: 9250, Lambda BCs: 6.377e+01, Lambda PDE: 5.240e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9300, It: 9300, Time: 1.24s, Learning Rate: 1.8e-04\n",
      "Epoch: 9300, It: 9300, Total Error: 3.788e+02, Total Loss: 1.335e+00\n",
      "Epoch: 9300, It: 9300, Relative L2: 2.273e-04, Relative Linfty: 9.505e-04\n",
      "Epoch: 9300, It: 9300, Loss_helmholtz: 3.788e+02, Loss BCs(u): 1.734e-02\n",
      "Epoch: 9300, It: 9300, Lambda BCs: 6.377e+01, Lambda PDE: 5.261e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9350, It: 9350, Time: 1.24s, Learning Rate: 1.8e-04\n",
      "Epoch: 9350, It: 9350, Total Error: 3.768e+02, Total Loss: 1.328e+00\n",
      "Epoch: 9350, It: 9350, Relative L2: 2.170e-04, Relative Linfty: 6.138e-04\n",
      "Epoch: 9350, It: 9350, Loss_helmholtz: 3.768e+02, Loss BCs(u): 1.725e-02\n",
      "Epoch: 9350, It: 9350, Lambda BCs: 6.377e+01, Lambda PDE: 5.279e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9400, It: 9400, Time: 1.24s, Learning Rate: 1.7e-04\n",
      "Epoch: 9400, It: 9400, Total Error: 3.748e+02, Total Loss: 1.321e+00\n",
      "Epoch: 9400, It: 9400, Relative L2: 2.598e-04, Relative Linfty: 1.013e-03\n",
      "Epoch: 9400, It: 9400, Loss_helmholtz: 3.748e+02, Loss BCs(u): 1.716e-02\n",
      "Epoch: 9400, It: 9400, Lambda BCs: 6.377e+01, Lambda PDE: 5.297e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9450, It: 9450, Time: 1.25s, Learning Rate: 1.7e-04\n",
      "Epoch: 9450, It: 9450, Total Error: 3.729e+02, Total Loss: 1.314e+00\n",
      "Epoch: 9450, It: 9450, Relative L2: 2.674e-04, Relative Linfty: 9.460e-04\n",
      "Epoch: 9450, It: 9450, Loss_helmholtz: 3.728e+02, Loss BCs(u): 1.707e-02\n",
      "Epoch: 9450, It: 9450, Lambda BCs: 6.377e+01, Lambda PDE: 5.315e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9500, It: 9500, Time: 1.24s, Learning Rate: 1.7e-04\n",
      "Epoch: 9500, It: 9500, Total Error: 3.709e+02, Total Loss: 1.307e+00\n",
      "Epoch: 9500, It: 9500, Relative L2: 2.307e-04, Relative Linfty: 7.461e-04\n",
      "Epoch: 9500, It: 9500, Loss_helmholtz: 3.709e+02, Loss BCs(u): 1.698e-02\n",
      "Epoch: 9500, It: 9500, Lambda BCs: 6.377e+01, Lambda PDE: 5.330e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9550, It: 9550, Time: 1.24s, Learning Rate: 1.7e-04\n",
      "Epoch: 9550, It: 9550, Total Error: 3.690e+02, Total Loss: 1.300e+00\n",
      "Epoch: 9550, It: 9550, Relative L2: 2.664e-04, Relative Linfty: 1.269e-03\n",
      "Epoch: 9550, It: 9550, Loss_helmholtz: 3.690e+02, Loss BCs(u): 1.689e-02\n",
      "Epoch: 9550, It: 9550, Lambda BCs: 6.377e+01, Lambda PDE: 5.351e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9600, It: 9600, Time: 1.24s, Learning Rate: 1.6e-04\n",
      "Epoch: 9600, It: 9600, Total Error: 3.671e+02, Total Loss: 1.294e+00\n",
      "Epoch: 9600, It: 9600, Relative L2: 2.824e-04, Relative Linfty: 1.404e-03\n",
      "Epoch: 9600, It: 9600, Loss_helmholtz: 3.671e+02, Loss BCs(u): 1.680e-02\n",
      "Epoch: 9600, It: 9600, Lambda BCs: 6.377e+01, Lambda PDE: 5.368e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9650, It: 9650, Time: 1.24s, Learning Rate: 1.6e-04\n",
      "Epoch: 9650, It: 9650, Total Error: 3.652e+02, Total Loss: 1.287e+00\n",
      "Epoch: 9650, It: 9650, Relative L2: 2.434e-04, Relative Linfty: 9.399e-04\n",
      "Epoch: 9650, It: 9650, Loss_helmholtz: 3.652e+02, Loss BCs(u): 1.672e-02\n",
      "Epoch: 9650, It: 9650, Lambda BCs: 6.377e+01, Lambda PDE: 5.385e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9700, It: 9700, Time: 1.24s, Learning Rate: 1.6e-04\n",
      "Epoch: 9700, It: 9700, Total Error: 3.633e+02, Total Loss: 1.280e+00\n",
      "Epoch: 9700, It: 9700, Relative L2: 1.942e-04, Relative Linfty: 5.157e-04\n",
      "Epoch: 9700, It: 9700, Loss_helmholtz: 3.633e+02, Loss BCs(u): 1.663e-02\n",
      "Epoch: 9700, It: 9700, Lambda BCs: 6.377e+01, Lambda PDE: 5.407e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9750, It: 9750, Time: 1.24s, Learning Rate: 1.5e-04\n",
      "Epoch: 9750, It: 9750, Total Error: 3.615e+02, Total Loss: 1.274e+00\n",
      "Epoch: 9750, It: 9750, Relative L2: 2.253e-04, Relative Linfty: 8.805e-04\n",
      "Epoch: 9750, It: 9750, Loss_helmholtz: 3.614e+02, Loss BCs(u): 1.654e-02\n",
      "Epoch: 9750, It: 9750, Lambda BCs: 6.377e+01, Lambda PDE: 5.429e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9800, It: 9800, Time: 1.24s, Learning Rate: 1.5e-04\n",
      "Epoch: 9800, It: 9800, Total Error: 3.596e+02, Total Loss: 1.267e+00\n",
      "Epoch: 9800, It: 9800, Relative L2: 2.112e-04, Relative Linfty: 1.043e-03\n",
      "Epoch: 9800, It: 9800, Loss_helmholtz: 3.596e+02, Loss BCs(u): 1.646e-02\n",
      "Epoch: 9800, It: 9800, Lambda BCs: 6.377e+01, Lambda PDE: 5.452e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9850, It: 9850, Time: 1.24s, Learning Rate: 1.5e-04\n",
      "Epoch: 9850, It: 9850, Total Error: 3.578e+02, Total Loss: 1.261e+00\n",
      "Epoch: 9850, It: 9850, Relative L2: 2.043e-04, Relative Linfty: 7.300e-04\n",
      "Epoch: 9850, It: 9850, Loss_helmholtz: 3.578e+02, Loss BCs(u): 1.638e-02\n",
      "Epoch: 9850, It: 9850, Lambda BCs: 6.377e+01, Lambda PDE: 5.478e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9900, It: 9900, Time: 1.25s, Learning Rate: 1.5e-04\n",
      "Epoch: 9900, It: 9900, Total Error: 3.560e+02, Total Loss: 1.255e+00\n",
      "Epoch: 9900, It: 9900, Relative L2: 2.053e-04, Relative Linfty: 6.754e-04\n",
      "Epoch: 9900, It: 9900, Loss_helmholtz: 3.560e+02, Loss BCs(u): 1.630e-02\n",
      "Epoch: 9900, It: 9900, Lambda BCs: 6.377e+01, Lambda PDE: 5.502e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 9950, It: 9950, Time: 1.24s, Learning Rate: 1.4e-04\n",
      "Epoch: 9950, It: 9950, Total Error: 3.542e+02, Total Loss: 1.248e+00\n",
      "Epoch: 9950, It: 9950, Relative L2: 2.082e-04, Relative Linfty: 7.692e-04\n",
      "Epoch: 9950, It: 9950, Loss_helmholtz: 3.542e+02, Loss BCs(u): 1.621e-02\n",
      "Epoch: 9950, It: 9950, Lambda BCs: 6.377e+01, Lambda PDE: 5.523e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10000, It: 10000, Time: 1.24s, Learning Rate: 1.4e-04\n",
      "Epoch: 10000, It: 10000, Total Error: 3.525e+02, Total Loss: 1.242e+00\n",
      "Epoch: 10000, It: 10000, Relative L2: 2.000e-04, Relative Linfty: 7.446e-04\n",
      "Epoch: 10000, It: 10000, Loss_helmholtz: 3.524e+02, Loss BCs(u): 1.613e-02\n",
      "Epoch: 10000, It: 10000, Lambda BCs: 6.377e+01, Lambda PDE: 5.552e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10050, It: 10050, Time: 1.24s, Learning Rate: 1.4e-04\n",
      "Epoch: 10050, It: 10050, Total Error: 3.507e+02, Total Loss: 1.236e+00\n",
      "Epoch: 10050, It: 10050, Relative L2: 2.339e-04, Relative Linfty: 9.609e-04\n",
      "Epoch: 10050, It: 10050, Loss_helmholtz: 3.507e+02, Loss BCs(u): 1.605e-02\n",
      "Epoch: 10050, It: 10050, Lambda BCs: 6.377e+01, Lambda PDE: 5.572e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10100, It: 10100, Time: 1.24s, Learning Rate: 1.4e-04\n",
      "Epoch: 10100, It: 10100, Total Error: 3.490e+02, Total Loss: 1.230e+00\n",
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      "Epoch: 10100, It: 10100, Loss_helmholtz: 3.490e+02, Loss BCs(u): 1.597e-02\n",
      "Epoch: 10100, It: 10100, Lambda BCs: 6.377e+01, Lambda PDE: 5.590e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10150, It: 10150, Time: 1.24s, Learning Rate: 1.3e-04\n",
      "Epoch: 10150, It: 10150, Total Error: 3.473e+02, Total Loss: 1.224e+00\n",
      "Epoch: 10150, It: 10150, Relative L2: 2.555e-04, Relative Linfty: 1.096e-03\n",
      "Epoch: 10150, It: 10150, Loss_helmholtz: 3.473e+02, Loss BCs(u): 1.590e-02\n",
      "Epoch: 10150, It: 10150, Lambda BCs: 6.377e+01, Lambda PDE: 5.614e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10200, It: 10200, Time: 1.24s, Learning Rate: 1.3e-04\n",
      "Epoch: 10200, It: 10200, Total Error: 3.456e+02, Total Loss: 1.218e+00\n",
      "Epoch: 10200, It: 10200, Relative L2: 2.104e-04, Relative Linfty: 6.927e-04\n",
      "Epoch: 10200, It: 10200, Loss_helmholtz: 3.456e+02, Loss BCs(u): 1.582e-02\n",
      "Epoch: 10200, It: 10200, Lambda BCs: 6.377e+01, Lambda PDE: 5.631e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10250, It: 10250, Time: 1.24s, Learning Rate: 1.3e-04\n",
      "Epoch: 10250, It: 10250, Total Error: 3.439e+02, Total Loss: 1.212e+00\n",
      "Epoch: 10250, It: 10250, Relative L2: 2.009e-04, Relative Linfty: 6.424e-04\n",
      "Epoch: 10250, It: 10250, Loss_helmholtz: 3.439e+02, Loss BCs(u): 1.574e-02\n",
      "Epoch: 10250, It: 10250, Lambda BCs: 6.377e+01, Lambda PDE: 5.656e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10300, It: 10300, Time: 1.24s, Learning Rate: 1.3e-04\n",
      "Epoch: 10300, It: 10300, Total Error: 3.422e+02, Total Loss: 1.206e+00\n",
      "Epoch: 10300, It: 10300, Relative L2: 2.219e-04, Relative Linfty: 6.967e-04\n",
      "Epoch: 10300, It: 10300, Loss_helmholtz: 3.422e+02, Loss BCs(u): 1.567e-02\n",
      "Epoch: 10300, It: 10300, Lambda BCs: 6.377e+01, Lambda PDE: 5.690e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10350, It: 10350, Time: 1.24s, Learning Rate: 1.2e-04\n",
      "Epoch: 10350, It: 10350, Total Error: 3.406e+02, Total Loss: 1.201e+00\n",
      "Epoch: 10350, It: 10350, Relative L2: 1.995e-04, Relative Linfty: 6.390e-04\n",
      "Epoch: 10350, It: 10350, Loss_helmholtz: 3.406e+02, Loss BCs(u): 1.559e-02\n",
      "Epoch: 10350, It: 10350, Lambda BCs: 6.377e+01, Lambda PDE: 5.716e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10400, It: 10400, Time: 1.24s, Learning Rate: 1.2e-04\n",
      "Epoch: 10400, It: 10400, Total Error: 3.390e+02, Total Loss: 1.195e+00\n",
      "Epoch: 10400, It: 10400, Relative L2: 1.960e-04, Relative Linfty: 6.742e-04\n",
      "Epoch: 10400, It: 10400, Loss_helmholtz: 3.390e+02, Loss BCs(u): 1.552e-02\n",
      "Epoch: 10400, It: 10400, Lambda BCs: 6.377e+01, Lambda PDE: 5.738e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10450, It: 10450, Time: 1.25s, Learning Rate: 1.2e-04\n",
      "Epoch: 10450, It: 10450, Total Error: 3.374e+02, Total Loss: 1.189e+00\n",
      "Epoch: 10450, It: 10450, Relative L2: 2.010e-04, Relative Linfty: 6.936e-04\n",
      "Epoch: 10450, It: 10450, Loss_helmholtz: 3.373e+02, Loss BCs(u): 1.544e-02\n",
      "Epoch: 10450, It: 10450, Lambda BCs: 6.377e+01, Lambda PDE: 5.769e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10500, It: 10500, Time: 1.24s, Learning Rate: 1.2e-04\n",
      "Epoch: 10500, It: 10500, Total Error: 3.358e+02, Total Loss: 1.183e+00\n",
      "Epoch: 10500, It: 10500, Relative L2: 2.096e-04, Relative Linfty: 6.808e-04\n",
      "Epoch: 10500, It: 10500, Loss_helmholtz: 3.357e+02, Loss BCs(u): 1.537e-02\n",
      "Epoch: 10500, It: 10500, Lambda BCs: 6.377e+01, Lambda PDE: 5.805e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10550, It: 10550, Time: 1.24s, Learning Rate: 1.2e-04\n",
      "Epoch: 10550, It: 10550, Total Error: 3.342e+02, Total Loss: 1.178e+00\n",
      "Epoch: 10550, It: 10550, Relative L2: 1.973e-04, Relative Linfty: 6.019e-04\n",
      "Epoch: 10550, It: 10550, Loss_helmholtz: 3.342e+02, Loss BCs(u): 1.530e-02\n",
      "Epoch: 10550, It: 10550, Lambda BCs: 6.377e+01, Lambda PDE: 5.834e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10600, It: 10600, Time: 1.24s, Learning Rate: 1.1e-04\n",
      "Epoch: 10600, It: 10600, Total Error: 3.326e+02, Total Loss: 1.172e+00\n",
      "Epoch: 10600, It: 10600, Relative L2: 2.090e-04, Relative Linfty: 6.054e-04\n",
      "Epoch: 10600, It: 10600, Loss_helmholtz: 3.326e+02, Loss BCs(u): 1.522e-02\n",
      "Epoch: 10600, It: 10600, Lambda BCs: 6.377e+01, Lambda PDE: 5.866e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10650, It: 10650, Time: 1.28s, Learning Rate: 1.1e-04\n",
      "Epoch: 10650, It: 10650, Total Error: 3.310e+02, Total Loss: 1.167e+00\n",
      "Epoch: 10650, It: 10650, Relative L2: 2.160e-04, Relative Linfty: 6.451e-04\n",
      "Epoch: 10650, It: 10650, Loss_helmholtz: 3.310e+02, Loss BCs(u): 1.515e-02\n",
      "Epoch: 10650, It: 10650, Lambda BCs: 6.377e+01, Lambda PDE: 5.890e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10700, It: 10700, Time: 1.24s, Learning Rate: 1.1e-04\n",
      "Epoch: 10700, It: 10700, Total Error: 3.295e+02, Total Loss: 1.162e+00\n",
      "Epoch: 10700, It: 10700, Relative L2: 2.162e-04, Relative Linfty: 6.864e-04\n",
      "Epoch: 10700, It: 10700, Loss_helmholtz: 3.295e+02, Loss BCs(u): 1.508e-02\n",
      "Epoch: 10700, It: 10700, Lambda BCs: 6.377e+01, Lambda PDE: 5.913e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10750, It: 10750, Time: 1.24s, Learning Rate: 1.1e-04\n",
      "Epoch: 10750, It: 10750, Total Error: 3.280e+02, Total Loss: 1.156e+00\n",
      "Epoch: 10750, It: 10750, Relative L2: 2.212e-04, Relative Linfty: 6.637e-04\n",
      "Epoch: 10750, It: 10750, Loss_helmholtz: 3.280e+02, Loss BCs(u): 1.501e-02\n",
      "Epoch: 10750, It: 10750, Lambda BCs: 6.377e+01, Lambda PDE: 5.942e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10800, It: 10800, Time: 1.24s, Learning Rate: 1.1e-04\n",
      "Epoch: 10800, It: 10800, Total Error: 3.265e+02, Total Loss: 1.151e+00\n",
      "Epoch: 10800, It: 10800, Relative L2: 1.892e-04, Relative Linfty: 4.817e-04\n",
      "Epoch: 10800, It: 10800, Loss_helmholtz: 3.265e+02, Loss BCs(u): 1.494e-02\n",
      "Epoch: 10800, It: 10800, Lambda BCs: 6.377e+01, Lambda PDE: 5.966e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10850, It: 10850, Time: 1.24s, Learning Rate: 1.0e-04\n",
      "Epoch: 10850, It: 10850, Total Error: 3.250e+02, Total Loss: 1.146e+00\n",
      "Epoch: 10850, It: 10850, Relative L2: 2.157e-04, Relative Linfty: 7.908e-04\n",
      "Epoch: 10850, It: 10850, Loss_helmholtz: 3.250e+02, Loss BCs(u): 1.488e-02\n",
      "Epoch: 10850, It: 10850, Lambda BCs: 6.377e+01, Lambda PDE: 5.990e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10900, It: 10900, Time: 1.24s, Learning Rate: 1.0e-04\n",
      "Epoch: 10900, It: 10900, Total Error: 3.235e+02, Total Loss: 1.140e+00\n",
      "Epoch: 10900, It: 10900, Relative L2: 1.946e-04, Relative Linfty: 5.565e-04\n",
      "Epoch: 10900, It: 10900, Loss_helmholtz: 3.235e+02, Loss BCs(u): 1.481e-02\n",
      "Epoch: 10900, It: 10900, Lambda BCs: 6.377e+01, Lambda PDE: 6.020e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 10950, It: 10950, Time: 1.24s, Learning Rate: 1.0e-04\n",
      "Epoch: 10950, It: 10950, Total Error: 3.220e+02, Total Loss: 1.135e+00\n",
      "Epoch: 10950, It: 10950, Relative L2: 2.228e-04, Relative Linfty: 6.292e-04\n",
      "Epoch: 10950, It: 10950, Loss_helmholtz: 3.220e+02, Loss BCs(u): 1.474e-02\n",
      "Epoch: 10950, It: 10950, Lambda BCs: 6.377e+01, Lambda PDE: 6.042e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11000, It: 11000, Time: 1.24s, Learning Rate: 9.9e-05\n",
      "Epoch: 11000, It: 11000, Total Error: 3.206e+02, Total Loss: 1.130e+00\n",
      "Epoch: 11000, It: 11000, Relative L2: 2.006e-04, Relative Linfty: 5.290e-04\n",
      "Epoch: 11000, It: 11000, Loss_helmholtz: 3.205e+02, Loss BCs(u): 1.467e-02\n",
      "Epoch: 11000, It: 11000, Lambda BCs: 6.377e+01, Lambda PDE: 6.066e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11050, It: 11050, Time: 1.24s, Learning Rate: 9.7e-05\n",
      "Epoch: 11050, It: 11050, Total Error: 3.191e+02, Total Loss: 1.125e+00\n",
      "Epoch: 11050, It: 11050, Relative L2: 1.860e-04, Relative Linfty: 5.354e-04\n",
      "Epoch: 11050, It: 11050, Loss_helmholtz: 3.191e+02, Loss BCs(u): 1.461e-02\n",
      "Epoch: 11050, It: 11050, Lambda BCs: 6.377e+01, Lambda PDE: 6.096e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11100, It: 11100, Time: 1.24s, Learning Rate: 9.5e-05\n",
      "Epoch: 11100, It: 11100, Total Error: 3.177e+02, Total Loss: 1.120e+00\n",
      "Epoch: 11100, It: 11100, Relative L2: 2.123e-04, Relative Linfty: 6.895e-04\n",
      "Epoch: 11100, It: 11100, Loss_helmholtz: 3.177e+02, Loss BCs(u): 1.454e-02\n",
      "Epoch: 11100, It: 11100, Lambda BCs: 6.377e+01, Lambda PDE: 6.129e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11150, It: 11150, Time: 1.24s, Learning Rate: 9.4e-05\n",
      "Epoch: 11150, It: 11150, Total Error: 3.163e+02, Total Loss: 1.115e+00\n",
      "Epoch: 11150, It: 11150, Relative L2: 1.982e-04, Relative Linfty: 5.521e-04\n",
      "Epoch: 11150, It: 11150, Loss_helmholtz: 3.163e+02, Loss BCs(u): 1.448e-02\n",
      "Epoch: 11150, It: 11150, Lambda BCs: 6.377e+01, Lambda PDE: 6.160e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11200, It: 11200, Time: 1.24s, Learning Rate: 9.2e-05\n",
      "Epoch: 11200, It: 11200, Total Error: 3.149e+02, Total Loss: 1.110e+00\n",
      "Epoch: 11200, It: 11200, Relative L2: 2.226e-04, Relative Linfty: 5.990e-04\n",
      "Epoch: 11200, It: 11200, Loss_helmholtz: 3.149e+02, Loss BCs(u): 1.441e-02\n",
      "Epoch: 11200, It: 11200, Lambda BCs: 6.377e+01, Lambda PDE: 6.197e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11250, It: 11250, Time: 1.24s, Learning Rate: 9.0e-05\n",
      "Epoch: 11250, It: 11250, Total Error: 3.135e+02, Total Loss: 1.105e+00\n",
      "Epoch: 11250, It: 11250, Relative L2: 2.163e-04, Relative Linfty: 6.162e-04\n",
      "Epoch: 11250, It: 11250, Loss_helmholtz: 3.135e+02, Loss BCs(u): 1.435e-02\n",
      "Epoch: 11250, It: 11250, Lambda BCs: 6.377e+01, Lambda PDE: 6.229e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11300, It: 11300, Time: 1.24s, Learning Rate: 8.9e-05\n",
      "Epoch: 11300, It: 11300, Total Error: 3.121e+02, Total Loss: 1.100e+00\n",
      "Epoch: 11300, It: 11300, Relative L2: 2.217e-04, Relative Linfty: 6.618e-04\n",
      "Epoch: 11300, It: 11300, Loss_helmholtz: 3.121e+02, Loss BCs(u): 1.429e-02\n",
      "Epoch: 11300, It: 11300, Lambda BCs: 6.377e+01, Lambda PDE: 6.263e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11350, It: 11350, Time: 1.24s, Learning Rate: 8.7e-05\n",
      "Epoch: 11350, It: 11350, Total Error: 3.107e+02, Total Loss: 1.095e+00\n",
      "Epoch: 11350, It: 11350, Relative L2: 2.266e-04, Relative Linfty: 6.273e-04\n",
      "Epoch: 11350, It: 11350, Loss_helmholtz: 3.107e+02, Loss BCs(u): 1.422e-02\n",
      "Epoch: 11350, It: 11350, Lambda BCs: 6.377e+01, Lambda PDE: 6.293e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11400, It: 11400, Time: 1.24s, Learning Rate: 8.6e-05\n",
      "Epoch: 11400, It: 11400, Total Error: 3.094e+02, Total Loss: 1.091e+00\n",
      "Epoch: 11400, It: 11400, Relative L2: 2.129e-04, Relative Linfty: 6.812e-04\n",
      "Epoch: 11400, It: 11400, Loss_helmholtz: 3.094e+02, Loss BCs(u): 1.416e-02\n",
      "Epoch: 11400, It: 11400, Lambda BCs: 6.377e+01, Lambda PDE: 6.319e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11450, It: 11450, Time: 1.24s, Learning Rate: 8.4e-05\n",
      "Epoch: 11450, It: 11450, Total Error: 3.080e+02, Total Loss: 1.086e+00\n",
      "Epoch: 11450, It: 11450, Relative L2: 2.127e-04, Relative Linfty: 6.509e-04\n",
      "Epoch: 11450, It: 11450, Loss_helmholtz: 3.080e+02, Loss BCs(u): 1.410e-02\n",
      "Epoch: 11450, It: 11450, Lambda BCs: 6.377e+01, Lambda PDE: 6.347e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11500, It: 11500, Time: 1.24s, Learning Rate: 8.3e-05\n",
      "Epoch: 11500, It: 11500, Total Error: 3.067e+02, Total Loss: 1.081e+00\n",
      "Epoch: 11500, It: 11500, Relative L2: 1.978e-04, Relative Linfty: 6.051e-04\n",
      "Epoch: 11500, It: 11500, Loss_helmholtz: 3.067e+02, Loss BCs(u): 1.404e-02\n",
      "Epoch: 11500, It: 11500, Lambda BCs: 6.377e+01, Lambda PDE: 6.381e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11550, It: 11550, Time: 1.24s, Learning Rate: 8.1e-05\n",
      "Epoch: 11550, It: 11550, Total Error: 3.054e+02, Total Loss: 1.077e+00\n",
      "Epoch: 11550, It: 11550, Relative L2: 1.904e-04, Relative Linfty: 4.867e-04\n",
      "Epoch: 11550, It: 11550, Loss_helmholtz: 3.054e+02, Loss BCs(u): 1.398e-02\n",
      "Epoch: 11550, It: 11550, Lambda BCs: 6.377e+01, Lambda PDE: 6.414e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11600, It: 11600, Time: 1.24s, Learning Rate: 8.0e-05\n",
      "Epoch: 11600, It: 11600, Total Error: 3.041e+02, Total Loss: 1.072e+00\n",
      "Epoch: 11600, It: 11600, Relative L2: 1.927e-04, Relative Linfty: 4.851e-04\n",
      "Epoch: 11600, It: 11600, Loss_helmholtz: 3.040e+02, Loss BCs(u): 1.392e-02\n",
      "Epoch: 11600, It: 11600, Lambda BCs: 6.377e+01, Lambda PDE: 6.444e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11650, It: 11650, Time: 1.24s, Learning Rate: 7.8e-05\n",
      "Epoch: 11650, It: 11650, Total Error: 3.028e+02, Total Loss: 1.067e+00\n",
      "Epoch: 11650, It: 11650, Relative L2: 2.050e-04, Relative Linfty: 7.281e-04\n",
      "Epoch: 11650, It: 11650, Loss_helmholtz: 3.027e+02, Loss BCs(u): 1.386e-02\n",
      "Epoch: 11650, It: 11650, Lambda BCs: 6.377e+01, Lambda PDE: 6.480e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11700, It: 11700, Time: 1.24s, Learning Rate: 7.7e-05\n",
      "Epoch: 11700, It: 11700, Total Error: 3.015e+02, Total Loss: 1.063e+00\n",
      "Epoch: 11700, It: 11700, Relative L2: 2.008e-04, Relative Linfty: 5.175e-04\n",
      "Epoch: 11700, It: 11700, Loss_helmholtz: 3.015e+02, Loss BCs(u): 1.380e-02\n",
      "Epoch: 11700, It: 11700, Lambda BCs: 6.377e+01, Lambda PDE: 6.515e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11750, It: 11750, Time: 1.24s, Learning Rate: 7.6e-05\n",
      "Epoch: 11750, It: 11750, Total Error: 3.002e+02, Total Loss: 1.058e+00\n",
      "Epoch: 11750, It: 11750, Relative L2: 1.916e-04, Relative Linfty: 6.327e-04\n",
      "Epoch: 11750, It: 11750, Loss_helmholtz: 3.002e+02, Loss BCs(u): 1.374e-02\n",
      "Epoch: 11750, It: 11750, Lambda BCs: 6.377e+01, Lambda PDE: 6.555e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11800, It: 11800, Time: 1.24s, Learning Rate: 7.4e-05\n",
      "Epoch: 11800, It: 11800, Total Error: 2.989e+02, Total Loss: 1.054e+00\n",
      "Epoch: 11800, It: 11800, Relative L2: 1.988e-04, Relative Linfty: 6.137e-04\n",
      "Epoch: 11800, It: 11800, Loss_helmholtz: 2.989e+02, Loss BCs(u): 1.368e-02\n",
      "Epoch: 11800, It: 11800, Lambda BCs: 6.377e+01, Lambda PDE: 6.595e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11850, It: 11850, Time: 1.24s, Learning Rate: 7.3e-05\n",
      "Epoch: 11850, It: 11850, Total Error: 2.977e+02, Total Loss: 1.049e+00\n",
      "Epoch: 11850, It: 11850, Relative L2: 2.032e-04, Relative Linfty: 5.694e-04\n",
      "Epoch: 11850, It: 11850, Loss_helmholtz: 2.977e+02, Loss BCs(u): 1.363e-02\n",
      "Epoch: 11850, It: 11850, Lambda BCs: 6.377e+01, Lambda PDE: 6.634e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11900, It: 11900, Time: 1.25s, Learning Rate: 7.2e-05\n",
      "Epoch: 11900, It: 11900, Total Error: 2.964e+02, Total Loss: 1.045e+00\n",
      "Epoch: 11900, It: 11900, Relative L2: 1.890e-04, Relative Linfty: 4.463e-04\n",
      "Epoch: 11900, It: 11900, Loss_helmholtz: 2.964e+02, Loss BCs(u): 1.357e-02\n",
      "Epoch: 11900, It: 11900, Lambda BCs: 6.377e+01, Lambda PDE: 6.673e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 11950, It: 11950, Time: 1.24s, Learning Rate: 7.0e-05\n",
      "Epoch: 11950, It: 11950, Total Error: 2.952e+02, Total Loss: 1.041e+00\n",
      "Epoch: 11950, It: 11950, Relative L2: 1.949e-04, Relative Linfty: 5.152e-04\n",
      "Epoch: 11950, It: 11950, Loss_helmholtz: 2.952e+02, Loss BCs(u): 1.351e-02\n",
      "Epoch: 11950, It: 11950, Lambda BCs: 6.377e+01, Lambda PDE: 6.721e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12000, It: 12000, Time: 1.24s, Learning Rate: 6.9e-05\n",
      "Epoch: 12000, It: 12000, Total Error: 2.940e+02, Total Loss: 1.036e+00\n",
      "Epoch: 12000, It: 12000, Relative L2: 1.925e-04, Relative Linfty: 4.492e-04\n",
      "Epoch: 12000, It: 12000, Loss_helmholtz: 2.939e+02, Loss BCs(u): 1.346e-02\n",
      "Epoch: 12000, It: 12000, Lambda BCs: 6.377e+01, Lambda PDE: 6.757e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12050, It: 12050, Time: 1.24s, Learning Rate: 6.8e-05\n",
      "Epoch: 12050, It: 12050, Total Error: 2.927e+02, Total Loss: 1.032e+00\n",
      "Epoch: 12050, It: 12050, Relative L2: 1.975e-04, Relative Linfty: 5.622e-04\n",
      "Epoch: 12050, It: 12050, Loss_helmholtz: 2.927e+02, Loss BCs(u): 1.340e-02\n",
      "Epoch: 12050, It: 12050, Lambda BCs: 6.377e+01, Lambda PDE: 6.793e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12100, It: 12100, Time: 1.24s, Learning Rate: 6.7e-05\n",
      "Epoch: 12100, It: 12100, Total Error: 2.915e+02, Total Loss: 1.028e+00\n",
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      "Epoch: 12100, It: 12100, Loss_helmholtz: 2.915e+02, Loss BCs(u): 1.334e-02\n",
      "Epoch: 12100, It: 12100, Lambda BCs: 6.377e+01, Lambda PDE: 6.841e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12150, It: 12150, Time: 1.24s, Learning Rate: 6.6e-05\n",
      "Epoch: 12150, It: 12150, Total Error: 2.903e+02, Total Loss: 1.024e+00\n",
      "Epoch: 12150, It: 12150, Relative L2: 1.691e-04, Relative Linfty: 5.363e-04\n",
      "Epoch: 12150, It: 12150, Loss_helmholtz: 2.903e+02, Loss BCs(u): 1.329e-02\n",
      "Epoch: 12150, It: 12150, Lambda BCs: 6.377e+01, Lambda PDE: 6.885e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12200, It: 12200, Time: 1.24s, Learning Rate: 6.4e-05\n",
      "Epoch: 12200, It: 12200, Total Error: 2.892e+02, Total Loss: 1.019e+00\n",
      "Epoch: 12200, It: 12200, Relative L2: 1.813e-04, Relative Linfty: 5.216e-04\n",
      "Epoch: 12200, It: 12200, Loss_helmholtz: 2.891e+02, Loss BCs(u): 1.324e-02\n",
      "Epoch: 12200, It: 12200, Lambda BCs: 6.377e+01, Lambda PDE: 6.927e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12250, It: 12250, Time: 1.25s, Learning Rate: 6.3e-05\n",
      "Epoch: 12250, It: 12250, Total Error: 2.880e+02, Total Loss: 1.015e+00\n",
      "Epoch: 12250, It: 12250, Relative L2: 1.860e-04, Relative Linfty: 5.327e-04\n",
      "Epoch: 12250, It: 12250, Loss_helmholtz: 2.880e+02, Loss BCs(u): 1.318e-02\n",
      "Epoch: 12250, It: 12250, Lambda BCs: 6.377e+01, Lambda PDE: 6.966e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12300, It: 12300, Time: 1.24s, Learning Rate: 6.2e-05\n",
      "Epoch: 12300, It: 12300, Total Error: 2.868e+02, Total Loss: 1.011e+00\n",
      "Epoch: 12300, It: 12300, Relative L2: 1.910e-04, Relative Linfty: 6.267e-04\n",
      "Epoch: 12300, It: 12300, Loss_helmholtz: 2.868e+02, Loss BCs(u): 1.313e-02\n",
      "Epoch: 12300, It: 12300, Lambda BCs: 6.377e+01, Lambda PDE: 7.009e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12350, It: 12350, Time: 1.24s, Learning Rate: 6.1e-05\n",
      "Epoch: 12350, It: 12350, Total Error: 2.857e+02, Total Loss: 1.007e+00\n",
      "Epoch: 12350, It: 12350, Relative L2: 1.734e-04, Relative Linfty: 4.640e-04\n",
      "Epoch: 12350, It: 12350, Loss_helmholtz: 2.857e+02, Loss BCs(u): 1.308e-02\n",
      "Epoch: 12350, It: 12350, Lambda BCs: 6.377e+01, Lambda PDE: 7.048e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12400, It: 12400, Time: 1.24s, Learning Rate: 6.0e-05\n",
      "Epoch: 12400, It: 12400, Total Error: 2.845e+02, Total Loss: 1.003e+00\n",
      "Epoch: 12400, It: 12400, Relative L2: 1.895e-04, Relative Linfty: 5.610e-04\n",
      "Epoch: 12400, It: 12400, Loss_helmholtz: 2.845e+02, Loss BCs(u): 1.302e-02\n",
      "Epoch: 12400, It: 12400, Lambda BCs: 6.377e+01, Lambda PDE: 7.093e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12450, It: 12450, Time: 1.30s, Learning Rate: 5.9e-05\n",
      "Epoch: 12450, It: 12450, Total Error: 2.834e+02, Total Loss: 9.991e-01\n",
      "Epoch: 12450, It: 12450, Relative L2: 1.760e-04, Relative Linfty: 4.970e-04\n",
      "Epoch: 12450, It: 12450, Loss_helmholtz: 2.834e+02, Loss BCs(u): 1.297e-02\n",
      "Epoch: 12450, It: 12450, Lambda BCs: 6.377e+01, Lambda PDE: 7.134e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12500, It: 12500, Time: 1.24s, Learning Rate: 5.8e-05\n",
      "Epoch: 12500, It: 12500, Total Error: 2.822e+02, Total Loss: 9.951e-01\n",
      "Epoch: 12500, It: 12500, Relative L2: 1.879e-04, Relative Linfty: 5.202e-04\n",
      "Epoch: 12500, It: 12500, Loss_helmholtz: 2.822e+02, Loss BCs(u): 1.292e-02\n",
      "Epoch: 12500, It: 12500, Lambda BCs: 6.377e+01, Lambda PDE: 7.178e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12550, It: 12550, Time: 1.24s, Learning Rate: 5.7e-05\n",
      "Epoch: 12550, It: 12550, Total Error: 2.811e+02, Total Loss: 9.912e-01\n",
      "Epoch: 12550, It: 12550, Relative L2: 1.936e-04, Relative Linfty: 6.086e-04\n",
      "Epoch: 12550, It: 12550, Loss_helmholtz: 2.811e+02, Loss BCs(u): 1.287e-02\n",
      "Epoch: 12550, It: 12550, Lambda BCs: 6.377e+01, Lambda PDE: 7.219e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12600, It: 12600, Time: 1.24s, Learning Rate: 5.6e-05\n",
      "Epoch: 12600, It: 12600, Total Error: 2.800e+02, Total Loss: 9.872e-01\n",
      "Epoch: 12600, It: 12600, Relative L2: 1.878e-04, Relative Linfty: 5.347e-04\n",
      "Epoch: 12600, It: 12600, Loss_helmholtz: 2.800e+02, Loss BCs(u): 1.282e-02\n",
      "Epoch: 12600, It: 12600, Lambda BCs: 6.377e+01, Lambda PDE: 7.259e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12650, It: 12650, Time: 1.24s, Learning Rate: 5.5e-05\n",
      "Epoch: 12650, It: 12650, Total Error: 2.789e+02, Total Loss: 9.834e-01\n",
      "Epoch: 12650, It: 12650, Relative L2: 1.812e-04, Relative Linfty: 4.794e-04\n",
      "Epoch: 12650, It: 12650, Loss_helmholtz: 2.789e+02, Loss BCs(u): 1.277e-02\n",
      "Epoch: 12650, It: 12650, Lambda BCs: 6.377e+01, Lambda PDE: 7.299e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12700, It: 12700, Time: 1.24s, Learning Rate: 5.4e-05\n",
      "Epoch: 12700, It: 12700, Total Error: 2.778e+02, Total Loss: 9.795e-01\n",
      "Epoch: 12700, It: 12700, Relative L2: 2.030e-04, Relative Linfty: 5.011e-04\n",
      "Epoch: 12700, It: 12700, Loss_helmholtz: 2.778e+02, Loss BCs(u): 1.272e-02\n",
      "Epoch: 12700, It: 12700, Lambda BCs: 6.377e+01, Lambda PDE: 7.331e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12750, It: 12750, Time: 1.24s, Learning Rate: 5.3e-05\n",
      "Epoch: 12750, It: 12750, Total Error: 2.767e+02, Total Loss: 9.757e-01\n",
      "Epoch: 12750, It: 12750, Relative L2: 1.765e-04, Relative Linfty: 5.046e-04\n",
      "Epoch: 12750, It: 12750, Loss_helmholtz: 2.767e+02, Loss BCs(u): 1.267e-02\n",
      "Epoch: 12750, It: 12750, Lambda BCs: 6.377e+01, Lambda PDE: 7.368e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12800, It: 12800, Time: 1.24s, Learning Rate: 5.2e-05\n",
      "Epoch: 12800, It: 12800, Total Error: 2.757e+02, Total Loss: 9.719e-01\n",
      "Epoch: 12800, It: 12800, Relative L2: 2.060e-04, Relative Linfty: 7.025e-04\n",
      "Epoch: 12800, It: 12800, Loss_helmholtz: 2.756e+02, Loss BCs(u): 1.262e-02\n",
      "Epoch: 12800, It: 12800, Lambda BCs: 6.377e+01, Lambda PDE: 7.412e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12850, It: 12850, Time: 1.24s, Learning Rate: 5.1e-05\n",
      "Epoch: 12850, It: 12850, Total Error: 2.746e+02, Total Loss: 9.681e-01\n",
      "Epoch: 12850, It: 12850, Relative L2: 1.912e-04, Relative Linfty: 5.896e-04\n",
      "Epoch: 12850, It: 12850, Loss_helmholtz: 2.746e+02, Loss BCs(u): 1.257e-02\n",
      "Epoch: 12850, It: 12850, Lambda BCs: 6.377e+01, Lambda PDE: 7.454e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12900, It: 12900, Time: 1.24s, Learning Rate: 5.0e-05\n",
      "Epoch: 12900, It: 12900, Total Error: 2.735e+02, Total Loss: 9.644e-01\n",
      "Epoch: 12900, It: 12900, Relative L2: 1.757e-04, Relative Linfty: 4.331e-04\n",
      "Epoch: 12900, It: 12900, Loss_helmholtz: 2.735e+02, Loss BCs(u): 1.252e-02\n",
      "Epoch: 12900, It: 12900, Lambda BCs: 6.377e+01, Lambda PDE: 7.494e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 12950, It: 12950, Time: 1.24s, Learning Rate: 4.9e-05\n",
      "Epoch: 12950, It: 12950, Total Error: 2.725e+02, Total Loss: 9.607e-01\n",
      "Epoch: 12950, It: 12950, Relative L2: 1.735e-04, Relative Linfty: 5.006e-04\n",
      "Epoch: 12950, It: 12950, Loss_helmholtz: 2.725e+02, Loss BCs(u): 1.247e-02\n",
      "Epoch: 12950, It: 12950, Lambda BCs: 6.377e+01, Lambda PDE: 7.534e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13000, It: 13000, Time: 1.25s, Learning Rate: 4.8e-05\n",
      "Epoch: 13000, It: 13000, Total Error: 2.714e+02, Total Loss: 9.570e-01\n",
      "Epoch: 13000, It: 13000, Relative L2: 1.757e-04, Relative Linfty: 4.206e-04\n",
      "Epoch: 13000, It: 13000, Loss_helmholtz: 2.714e+02, Loss BCs(u): 1.242e-02\n",
      "Epoch: 13000, It: 13000, Lambda BCs: 6.377e+01, Lambda PDE: 7.570e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13050, It: 13050, Time: 1.24s, Learning Rate: 4.8e-05\n",
      "Epoch: 13050, It: 13050, Total Error: 2.704e+02, Total Loss: 9.534e-01\n",
      "Epoch: 13050, It: 13050, Relative L2: 1.752e-04, Relative Linfty: 4.358e-04\n",
      "Epoch: 13050, It: 13050, Loss_helmholtz: 2.704e+02, Loss BCs(u): 1.238e-02\n",
      "Epoch: 13050, It: 13050, Lambda BCs: 6.377e+01, Lambda PDE: 7.611e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13100, It: 13100, Time: 1.24s, Learning Rate: 4.7e-05\n",
      "Epoch: 13100, It: 13100, Total Error: 2.694e+02, Total Loss: 9.497e-01\n",
      "Epoch: 13100, It: 13100, Relative L2: 1.825e-04, Relative Linfty: 4.533e-04\n",
      "Epoch: 13100, It: 13100, Loss_helmholtz: 2.694e+02, Loss BCs(u): 1.233e-02\n",
      "Epoch: 13100, It: 13100, Lambda BCs: 6.377e+01, Lambda PDE: 7.657e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13150, It: 13150, Time: 1.24s, Learning Rate: 4.6e-05\n",
      "Epoch: 13150, It: 13150, Total Error: 2.684e+02, Total Loss: 9.462e-01\n",
      "Epoch: 13150, It: 13150, Relative L2: 1.669e-04, Relative Linfty: 4.479e-04\n",
      "Epoch: 13150, It: 13150, Loss_helmholtz: 2.683e+02, Loss BCs(u): 1.228e-02\n",
      "Epoch: 13150, It: 13150, Lambda BCs: 6.377e+01, Lambda PDE: 7.699e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13200, It: 13200, Time: 1.24s, Learning Rate: 4.5e-05\n",
      "Epoch: 13200, It: 13200, Total Error: 2.673e+02, Total Loss: 9.426e-01\n",
      "Epoch: 13200, It: 13200, Relative L2: 1.730e-04, Relative Linfty: 4.190e-04\n",
      "Epoch: 13200, It: 13200, Loss_helmholtz: 2.673e+02, Loss BCs(u): 1.224e-02\n",
      "Epoch: 13200, It: 13200, Lambda BCs: 6.377e+01, Lambda PDE: 7.744e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13250, It: 13250, Time: 1.24s, Learning Rate: 4.4e-05\n",
      "Epoch: 13250, It: 13250, Total Error: 2.663e+02, Total Loss: 9.390e-01\n",
      "Epoch: 13250, It: 13250, Relative L2: 1.713e-04, Relative Linfty: 4.417e-04\n",
      "Epoch: 13250, It: 13250, Loss_helmholtz: 2.663e+02, Loss BCs(u): 1.219e-02\n",
      "Epoch: 13250, It: 13250, Lambda BCs: 6.377e+01, Lambda PDE: 7.788e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13300, It: 13300, Time: 1.25s, Learning Rate: 4.4e-05\n",
      "Epoch: 13300, It: 13300, Total Error: 2.653e+02, Total Loss: 9.355e-01\n",
      "Epoch: 13300, It: 13300, Relative L2: 1.798e-04, Relative Linfty: 4.945e-04\n",
      "Epoch: 13300, It: 13300, Loss_helmholtz: 2.653e+02, Loss BCs(u): 1.215e-02\n",
      "Epoch: 13300, It: 13300, Lambda BCs: 6.377e+01, Lambda PDE: 7.827e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13350, It: 13350, Time: 1.24s, Learning Rate: 4.3e-05\n",
      "Epoch: 13350, It: 13350, Total Error: 2.643e+02, Total Loss: 9.320e-01\n",
      "Epoch: 13350, It: 13350, Relative L2: 1.701e-04, Relative Linfty: 4.178e-04\n",
      "Epoch: 13350, It: 13350, Loss_helmholtz: 2.643e+02, Loss BCs(u): 1.210e-02\n",
      "Epoch: 13350, It: 13350, Lambda BCs: 6.377e+01, Lambda PDE: 7.875e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13400, It: 13400, Time: 1.24s, Learning Rate: 4.2e-05\n",
      "Epoch: 13400, It: 13400, Total Error: 2.634e+02, Total Loss: 9.286e-01\n",
      "Epoch: 13400, It: 13400, Relative L2: 1.691e-04, Relative Linfty: 4.315e-04\n",
      "Epoch: 13400, It: 13400, Loss_helmholtz: 2.634e+02, Loss BCs(u): 1.206e-02\n",
      "Epoch: 13400, It: 13400, Lambda BCs: 6.377e+01, Lambda PDE: 7.924e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13450, It: 13450, Time: 1.27s, Learning Rate: 4.1e-05\n",
      "Epoch: 13450, It: 13450, Total Error: 2.624e+02, Total Loss: 9.251e-01\n",
      "Epoch: 13450, It: 13450, Relative L2: 1.659e-04, Relative Linfty: 4.003e-04\n",
      "Epoch: 13450, It: 13450, Loss_helmholtz: 2.624e+02, Loss BCs(u): 1.201e-02\n",
      "Epoch: 13450, It: 13450, Lambda BCs: 6.377e+01, Lambda PDE: 7.972e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13500, It: 13500, Time: 1.24s, Learning Rate: 4.1e-05\n",
      "Epoch: 13500, It: 13500, Total Error: 2.614e+02, Total Loss: 9.217e-01\n",
      "Epoch: 13500, It: 13500, Relative L2: 1.756e-04, Relative Linfty: 5.090e-04\n",
      "Epoch: 13500, It: 13500, Loss_helmholtz: 2.614e+02, Loss BCs(u): 1.197e-02\n",
      "Epoch: 13500, It: 13500, Lambda BCs: 6.377e+01, Lambda PDE: 8.017e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13550, It: 13550, Time: 1.24s, Learning Rate: 4.0e-05\n",
      "Epoch: 13550, It: 13550, Total Error: 2.605e+02, Total Loss: 9.184e-01\n",
      "Epoch: 13550, It: 13550, Relative L2: 1.748e-04, Relative Linfty: 4.467e-04\n",
      "Epoch: 13550, It: 13550, Loss_helmholtz: 2.604e+02, Loss BCs(u): 1.192e-02\n",
      "Epoch: 13550, It: 13550, Lambda BCs: 6.377e+01, Lambda PDE: 8.057e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13600, It: 13600, Time: 1.24s, Learning Rate: 3.9e-05\n",
      "Epoch: 13600, It: 13600, Total Error: 2.595e+02, Total Loss: 9.150e-01\n",
      "Epoch: 13600, It: 13600, Relative L2: 1.792e-04, Relative Linfty: 4.172e-04\n",
      "Epoch: 13600, It: 13600, Loss_helmholtz: 2.595e+02, Loss BCs(u): 1.188e-02\n",
      "Epoch: 13600, It: 13600, Lambda BCs: 6.377e+01, Lambda PDE: 8.100e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13650, It: 13650, Time: 1.24s, Learning Rate: 3.8e-05\n",
      "Epoch: 13650, It: 13650, Total Error: 2.586e+02, Total Loss: 9.117e-01\n",
      "Epoch: 13650, It: 13650, Relative L2: 1.687e-04, Relative Linfty: 4.402e-04\n",
      "Epoch: 13650, It: 13650, Loss_helmholtz: 2.585e+02, Loss BCs(u): 1.184e-02\n",
      "Epoch: 13650, It: 13650, Lambda BCs: 6.377e+01, Lambda PDE: 8.146e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13700, It: 13700, Time: 1.24s, Learning Rate: 3.8e-05\n",
      "Epoch: 13700, It: 13700, Total Error: 2.576e+02, Total Loss: 9.083e-01\n",
      "Epoch: 13700, It: 13700, Relative L2: 1.723e-04, Relative Linfty: 5.603e-04\n",
      "Epoch: 13700, It: 13700, Loss_helmholtz: 2.576e+02, Loss BCs(u): 1.179e-02\n",
      "Epoch: 13700, It: 13700, Lambda BCs: 6.377e+01, Lambda PDE: 8.195e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13750, It: 13750, Time: 1.24s, Learning Rate: 3.7e-05\n",
      "Epoch: 13750, It: 13750, Total Error: 2.567e+02, Total Loss: 9.051e-01\n",
      "Epoch: 13750, It: 13750, Relative L2: 1.771e-04, Relative Linfty: 4.868e-04\n",
      "Epoch: 13750, It: 13750, Loss_helmholtz: 2.567e+02, Loss BCs(u): 1.175e-02\n",
      "Epoch: 13750, It: 13750, Lambda BCs: 6.377e+01, Lambda PDE: 8.239e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13800, It: 13800, Time: 1.24s, Learning Rate: 3.6e-05\n",
      "Epoch: 13800, It: 13800, Total Error: 2.558e+02, Total Loss: 9.018e-01\n",
      "Epoch: 13800, It: 13800, Relative L2: 1.743e-04, Relative Linfty: 4.205e-04\n",
      "Epoch: 13800, It: 13800, Loss_helmholtz: 2.557e+02, Loss BCs(u): 1.171e-02\n",
      "Epoch: 13800, It: 13800, Lambda BCs: 6.377e+01, Lambda PDE: 8.278e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13850, It: 13850, Time: 1.24s, Learning Rate: 3.6e-05\n",
      "Epoch: 13850, It: 13850, Total Error: 2.548e+02, Total Loss: 8.986e-01\n",
      "Epoch: 13850, It: 13850, Relative L2: 1.648e-04, Relative Linfty: 4.009e-04\n",
      "Epoch: 13850, It: 13850, Loss_helmholtz: 2.548e+02, Loss BCs(u): 1.166e-02\n",
      "Epoch: 13850, It: 13850, Lambda BCs: 6.377e+01, Lambda PDE: 8.323e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13900, It: 13900, Time: 1.24s, Learning Rate: 3.5e-05\n",
      "Epoch: 13900, It: 13900, Total Error: 2.539e+02, Total Loss: 8.953e-01\n",
      "Epoch: 13900, It: 13900, Relative L2: 1.757e-04, Relative Linfty: 4.490e-04\n",
      "Epoch: 13900, It: 13900, Loss_helmholtz: 2.539e+02, Loss BCs(u): 1.162e-02\n",
      "Epoch: 13900, It: 13900, Lambda BCs: 6.377e+01, Lambda PDE: 8.371e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 13950, It: 13950, Time: 1.24s, Learning Rate: 3.5e-05\n",
      "Epoch: 13950, It: 13950, Total Error: 2.530e+02, Total Loss: 8.921e-01\n",
      "Epoch: 13950, It: 13950, Relative L2: 1.676e-04, Relative Linfty: 4.130e-04\n",
      "Epoch: 13950, It: 13950, Loss_helmholtz: 2.530e+02, Loss BCs(u): 1.158e-02\n",
      "Epoch: 13950, It: 13950, Lambda BCs: 6.377e+01, Lambda PDE: 8.417e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14000, It: 14000, Time: 1.24s, Learning Rate: 3.4e-05\n",
      "Epoch: 14000, It: 14000, Total Error: 2.521e+02, Total Loss: 8.890e-01\n",
      "Epoch: 14000, It: 14000, Relative L2: 1.625e-04, Relative Linfty: 3.879e-04\n",
      "Epoch: 14000, It: 14000, Loss_helmholtz: 2.521e+02, Loss BCs(u): 1.154e-02\n",
      "Epoch: 14000, It: 14000, Lambda BCs: 6.377e+01, Lambda PDE: 8.462e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14050, It: 14050, Time: 1.24s, Learning Rate: 3.3e-05\n",
      "Epoch: 14050, It: 14050, Total Error: 2.512e+02, Total Loss: 8.858e-01\n",
      "Epoch: 14050, It: 14050, Relative L2: 1.697e-04, Relative Linfty: 4.425e-04\n",
      "Epoch: 14050, It: 14050, Loss_helmholtz: 2.512e+02, Loss BCs(u): 1.150e-02\n",
      "Epoch: 14050, It: 14050, Lambda BCs: 6.377e+01, Lambda PDE: 8.509e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14100, It: 14100, Time: 1.24s, Learning Rate: 3.3e-05\n",
      "Epoch: 14100, It: 14100, Total Error: 2.503e+02, Total Loss: 8.827e-01\n",
      "Epoch: 14100, It: 14100, Relative L2: 1.739e-04, Relative Linfty: 4.906e-04\n",
      "Epoch: 14100, It: 14100, Loss_helmholtz: 2.503e+02, Loss BCs(u): 1.146e-02\n",
      "Epoch: 14100, It: 14100, Lambda BCs: 6.377e+01, Lambda PDE: 8.554e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14150, It: 14150, Time: 1.24s, Learning Rate: 3.2e-05\n",
      "Epoch: 14150, It: 14150, Total Error: 2.495e+02, Total Loss: 8.796e-01\n",
      "Epoch: 14150, It: 14150, Relative L2: 1.681e-04, Relative Linfty: 4.357e-04\n",
      "Epoch: 14150, It: 14150, Loss_helmholtz: 2.494e+02, Loss BCs(u): 1.142e-02\n",
      "Epoch: 14150, It: 14150, Lambda BCs: 6.377e+01, Lambda PDE: 8.596e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14200, It: 14200, Time: 1.24s, Learning Rate: 3.2e-05\n",
      "Epoch: 14200, It: 14200, Total Error: 2.486e+02, Total Loss: 8.765e-01\n",
      "Epoch: 14200, It: 14200, Relative L2: 1.730e-04, Relative Linfty: 5.347e-04\n",
      "Epoch: 14200, It: 14200, Loss_helmholtz: 2.486e+02, Loss BCs(u): 1.138e-02\n",
      "Epoch: 14200, It: 14200, Lambda BCs: 6.377e+01, Lambda PDE: 8.645e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14250, It: 14250, Time: 1.24s, Learning Rate: 3.1e-05\n",
      "Epoch: 14250, It: 14250, Total Error: 2.477e+02, Total Loss: 8.734e-01\n",
      "Epoch: 14250, It: 14250, Relative L2: 1.651e-04, Relative Linfty: 4.893e-04\n",
      "Epoch: 14250, It: 14250, Loss_helmholtz: 2.477e+02, Loss BCs(u): 1.134e-02\n",
      "Epoch: 14250, It: 14250, Lambda BCs: 6.377e+01, Lambda PDE: 8.694e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14300, It: 14300, Time: 1.24s, Learning Rate: 3.0e-05\n",
      "Epoch: 14300, It: 14300, Total Error: 2.468e+02, Total Loss: 8.704e-01\n",
      "Epoch: 14300, It: 14300, Relative L2: 1.581e-04, Relative Linfty: 4.036e-04\n",
      "Epoch: 14300, It: 14300, Loss_helmholtz: 2.468e+02, Loss BCs(u): 1.130e-02\n",
      "Epoch: 14300, It: 14300, Lambda BCs: 6.377e+01, Lambda PDE: 8.737e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14350, It: 14350, Time: 1.24s, Learning Rate: 3.0e-05\n",
      "Epoch: 14350, It: 14350, Total Error: 2.460e+02, Total Loss: 8.674e-01\n",
      "Epoch: 14350, It: 14350, Relative L2: 1.654e-04, Relative Linfty: 4.748e-04\n",
      "Epoch: 14350, It: 14350, Loss_helmholtz: 2.460e+02, Loss BCs(u): 1.126e-02\n",
      "Epoch: 14350, It: 14350, Lambda BCs: 6.377e+01, Lambda PDE: 8.785e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14400, It: 14400, Time: 1.24s, Learning Rate: 2.9e-05\n",
      "Epoch: 14400, It: 14400, Total Error: 2.451e+02, Total Loss: 8.644e-01\n",
      "Epoch: 14400, It: 14400, Relative L2: 1.698e-04, Relative Linfty: 5.449e-04\n",
      "Epoch: 14400, It: 14400, Loss_helmholtz: 2.451e+02, Loss BCs(u): 1.122e-02\n",
      "Epoch: 14400, It: 14400, Lambda BCs: 6.377e+01, Lambda PDE: 8.833e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14450, It: 14450, Time: 1.24s, Learning Rate: 2.9e-05\n",
      "Epoch: 14450, It: 14450, Total Error: 2.443e+02, Total Loss: 8.614e-01\n",
      "Epoch: 14450, It: 14450, Relative L2: 1.554e-04, Relative Linfty: 3.996e-04\n",
      "Epoch: 14450, It: 14450, Loss_helmholtz: 2.443e+02, Loss BCs(u): 1.118e-02\n",
      "Epoch: 14450, It: 14450, Lambda BCs: 6.377e+01, Lambda PDE: 8.880e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14500, It: 14500, Time: 1.24s, Learning Rate: 2.8e-05\n",
      "Epoch: 14500, It: 14500, Total Error: 2.435e+02, Total Loss: 8.585e-01\n",
      "Epoch: 14500, It: 14500, Relative L2: 1.613e-04, Relative Linfty: 4.157e-04\n",
      "Epoch: 14500, It: 14500, Loss_helmholtz: 2.434e+02, Loss BCs(u): 1.114e-02\n",
      "Epoch: 14500, It: 14500, Lambda BCs: 6.377e+01, Lambda PDE: 8.926e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14550, It: 14550, Time: 1.24s, Learning Rate: 2.8e-05\n",
      "Epoch: 14550, It: 14550, Total Error: 2.426e+02, Total Loss: 8.555e-01\n",
      "Epoch: 14550, It: 14550, Relative L2: 1.683e-04, Relative Linfty: 4.360e-04\n",
      "Epoch: 14550, It: 14550, Loss_helmholtz: 2.426e+02, Loss BCs(u): 1.111e-02\n",
      "Epoch: 14550, It: 14550, Lambda BCs: 6.377e+01, Lambda PDE: 8.972e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14600, It: 14600, Time: 1.25s, Learning Rate: 2.7e-05\n",
      "Epoch: 14600, It: 14600, Total Error: 2.418e+02, Total Loss: 8.526e-01\n",
      "Epoch: 14600, It: 14600, Relative L2: 1.635e-04, Relative Linfty: 5.142e-04\n",
      "Epoch: 14600, It: 14600, Loss_helmholtz: 2.418e+02, Loss BCs(u): 1.107e-02\n",
      "Epoch: 14600, It: 14600, Lambda BCs: 6.377e+01, Lambda PDE: 9.020e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14650, It: 14650, Time: 1.24s, Learning Rate: 2.7e-05\n",
      "Epoch: 14650, It: 14650, Total Error: 2.410e+02, Total Loss: 8.497e-01\n",
      "Epoch: 14650, It: 14650, Relative L2: 1.644e-04, Relative Linfty: 4.348e-04\n",
      "Epoch: 14650, It: 14650, Loss_helmholtz: 2.410e+02, Loss BCs(u): 1.103e-02\n",
      "Epoch: 14650, It: 14650, Lambda BCs: 6.377e+01, Lambda PDE: 9.066e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14700, It: 14700, Time: 1.24s, Learning Rate: 2.6e-05\n",
      "Epoch: 14700, It: 14700, Total Error: 2.402e+02, Total Loss: 8.468e-01\n",
      "Epoch: 14700, It: 14700, Relative L2: 1.584e-04, Relative Linfty: 4.277e-04\n",
      "Epoch: 14700, It: 14700, Loss_helmholtz: 2.401e+02, Loss BCs(u): 1.099e-02\n",
      "Epoch: 14700, It: 14700, Lambda BCs: 6.377e+01, Lambda PDE: 9.111e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14750, It: 14750, Time: 1.24s, Learning Rate: 2.6e-05\n",
      "Epoch: 14750, It: 14750, Total Error: 2.393e+02, Total Loss: 8.440e-01\n",
      "Epoch: 14750, It: 14750, Relative L2: 1.689e-04, Relative Linfty: 4.553e-04\n",
      "Epoch: 14750, It: 14750, Loss_helmholtz: 2.393e+02, Loss BCs(u): 1.096e-02\n",
      "Epoch: 14750, It: 14750, Lambda BCs: 6.377e+01, Lambda PDE: 9.161e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14800, It: 14800, Time: 1.25s, Learning Rate: 2.5e-05\n",
      "Epoch: 14800, It: 14800, Total Error: 2.385e+02, Total Loss: 8.411e-01\n",
      "Epoch: 14800, It: 14800, Relative L2: 1.596e-04, Relative Linfty: 4.124e-04\n",
      "Epoch: 14800, It: 14800, Loss_helmholtz: 2.385e+02, Loss BCs(u): 1.092e-02\n",
      "Epoch: 14800, It: 14800, Lambda BCs: 6.377e+01, Lambda PDE: 9.208e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14850, It: 14850, Time: 1.24s, Learning Rate: 2.5e-05\n",
      "Epoch: 14850, It: 14850, Total Error: 2.377e+02, Total Loss: 8.383e-01\n",
      "Epoch: 14850, It: 14850, Relative L2: 1.524e-04, Relative Linfty: 4.461e-04\n",
      "Epoch: 14850, It: 14850, Loss_helmholtz: 2.377e+02, Loss BCs(u): 1.088e-02\n",
      "Epoch: 14850, It: 14850, Lambda BCs: 6.377e+01, Lambda PDE: 9.256e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14900, It: 14900, Time: 1.24s, Learning Rate: 2.5e-05\n",
      "Epoch: 14900, It: 14900, Total Error: 2.369e+02, Total Loss: 8.355e-01\n",
      "Epoch: 14900, It: 14900, Relative L2: 1.533e-04, Relative Linfty: 4.045e-04\n",
      "Epoch: 14900, It: 14900, Loss_helmholtz: 2.369e+02, Loss BCs(u): 1.085e-02\n",
      "Epoch: 14900, It: 14900, Lambda BCs: 6.377e+01, Lambda PDE: 9.304e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 14950, It: 14950, Time: 1.24s, Learning Rate: 2.4e-05\n",
      "Epoch: 14950, It: 14950, Total Error: 2.361e+02, Total Loss: 8.327e-01\n",
      "Epoch: 14950, It: 14950, Relative L2: 1.559e-04, Relative Linfty: 4.276e-04\n",
      "Epoch: 14950, It: 14950, Loss_helmholtz: 2.361e+02, Loss BCs(u): 1.081e-02\n",
      "Epoch: 14950, It: 14950, Lambda BCs: 6.377e+01, Lambda PDE: 9.350e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15000, It: 15000, Time: 1.24s, Learning Rate: 2.4e-05\n",
      "Epoch: 15000, It: 15000, Total Error: 2.354e+02, Total Loss: 8.300e-01\n",
      "Epoch: 15000, It: 15000, Relative L2: 1.486e-04, Relative Linfty: 5.120e-04\n",
      "Epoch: 15000, It: 15000, Loss_helmholtz: 2.354e+02, Loss BCs(u): 1.077e-02\n",
      "Epoch: 15000, It: 15000, Lambda BCs: 6.377e+01, Lambda PDE: 9.394e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15050, It: 15050, Time: 1.24s, Learning Rate: 2.3e-05\n",
      "Epoch: 15050, It: 15050, Total Error: 2.346e+02, Total Loss: 8.272e-01\n",
      "Epoch: 15050, It: 15050, Relative L2: 1.446e-04, Relative Linfty: 4.589e-04\n",
      "Epoch: 15050, It: 15050, Loss_helmholtz: 2.346e+02, Loss BCs(u): 1.074e-02\n",
      "Epoch: 15050, It: 15050, Lambda BCs: 6.377e+01, Lambda PDE: 9.440e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15100, It: 15100, Time: 1.24s, Learning Rate: 2.3e-05\n",
      "Epoch: 15100, It: 15100, Total Error: 2.338e+02, Total Loss: 8.245e-01\n",
      "Epoch: 15100, It: 15100, Relative L2: 1.480e-04, Relative Linfty: 4.149e-04\n",
      "Epoch: 15100, It: 15100, Loss_helmholtz: 2.338e+02, Loss BCs(u): 1.070e-02\n",
      "Epoch: 15100, It: 15100, Lambda BCs: 6.377e+01, Lambda PDE: 9.488e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15150, It: 15150, Time: 1.24s, Learning Rate: 2.3e-05\n",
      "Epoch: 15150, It: 15150, Total Error: 2.330e+02, Total Loss: 8.218e-01\n",
      "Epoch: 15150, It: 15150, Relative L2: 1.481e-04, Relative Linfty: 5.021e-04\n",
      "Epoch: 15150, It: 15150, Loss_helmholtz: 2.330e+02, Loss BCs(u): 1.067e-02\n",
      "Epoch: 15150, It: 15150, Lambda BCs: 6.377e+01, Lambda PDE: 9.535e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15200, It: 15200, Time: 1.24s, Learning Rate: 2.2e-05\n",
      "Epoch: 15200, It: 15200, Total Error: 2.323e+02, Total Loss: 8.191e-01\n",
      "Epoch: 15200, It: 15200, Relative L2: 1.460e-04, Relative Linfty: 3.962e-04\n",
      "Epoch: 15200, It: 15200, Loss_helmholtz: 2.323e+02, Loss BCs(u): 1.063e-02\n",
      "Epoch: 15200, It: 15200, Lambda BCs: 6.377e+01, Lambda PDE: 9.582e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15250, It: 15250, Time: 1.24s, Learning Rate: 2.2e-05\n",
      "Epoch: 15250, It: 15250, Total Error: 2.315e+02, Total Loss: 8.164e-01\n",
      "Epoch: 15250, It: 15250, Relative L2: 1.447e-04, Relative Linfty: 4.146e-04\n",
      "Epoch: 15250, It: 15250, Loss_helmholtz: 2.315e+02, Loss BCs(u): 1.060e-02\n",
      "Epoch: 15250, It: 15250, Lambda BCs: 6.377e+01, Lambda PDE: 9.632e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15300, It: 15300, Time: 1.25s, Learning Rate: 2.1e-05\n",
      "Epoch: 15300, It: 15300, Total Error: 2.308e+02, Total Loss: 8.138e-01\n",
      "Epoch: 15300, It: 15300, Relative L2: 1.411e-04, Relative Linfty: 4.137e-04\n",
      "Epoch: 15300, It: 15300, Loss_helmholtz: 2.308e+02, Loss BCs(u): 1.056e-02\n",
      "Epoch: 15300, It: 15300, Lambda BCs: 6.377e+01, Lambda PDE: 9.682e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15350, It: 15350, Time: 1.24s, Learning Rate: 2.1e-05\n",
      "Epoch: 15350, It: 15350, Total Error: 2.300e+02, Total Loss: 8.111e-01\n",
      "Epoch: 15350, It: 15350, Relative L2: 1.438e-04, Relative Linfty: 4.196e-04\n",
      "Epoch: 15350, It: 15350, Loss_helmholtz: 2.300e+02, Loss BCs(u): 1.053e-02\n",
      "Epoch: 15350, It: 15350, Lambda BCs: 6.377e+01, Lambda PDE: 9.730e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15400, It: 15400, Time: 1.24s, Learning Rate: 2.1e-05\n",
      "Epoch: 15400, It: 15400, Total Error: 2.293e+02, Total Loss: 8.085e-01\n",
      "Epoch: 15400, It: 15400, Relative L2: 1.467e-04, Relative Linfty: 3.915e-04\n",
      "Epoch: 15400, It: 15400, Loss_helmholtz: 2.293e+02, Loss BCs(u): 1.049e-02\n",
      "Epoch: 15400, It: 15400, Lambda BCs: 6.377e+01, Lambda PDE: 9.780e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15450, It: 15450, Time: 1.24s, Learning Rate: 2.0e-05\n",
      "Epoch: 15450, It: 15450, Total Error: 2.285e+02, Total Loss: 8.059e-01\n",
      "Epoch: 15450, It: 15450, Relative L2: 1.498e-04, Relative Linfty: 3.991e-04\n",
      "Epoch: 15450, It: 15450, Loss_helmholtz: 2.285e+02, Loss BCs(u): 1.046e-02\n",
      "Epoch: 15450, It: 15450, Lambda BCs: 6.377e+01, Lambda PDE: 9.826e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15500, It: 15500, Time: 1.24s, Learning Rate: 2.0e-05\n",
      "Epoch: 15500, It: 15500, Total Error: 2.278e+02, Total Loss: 8.033e-01\n",
      "Epoch: 15500, It: 15500, Relative L2: 1.418e-04, Relative Linfty: 3.765e-04\n",
      "Epoch: 15500, It: 15500, Loss_helmholtz: 2.278e+02, Loss BCs(u): 1.043e-02\n",
      "Epoch: 15500, It: 15500, Lambda BCs: 6.377e+01, Lambda PDE: 9.874e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15550, It: 15550, Time: 1.24s, Learning Rate: 2.0e-05\n",
      "Epoch: 15550, It: 15550, Total Error: 2.271e+02, Total Loss: 8.007e-01\n",
      "Epoch: 15550, It: 15550, Relative L2: 1.428e-04, Relative Linfty: 4.822e-04\n",
      "Epoch: 15550, It: 15550, Loss_helmholtz: 2.271e+02, Loss BCs(u): 1.039e-02\n",
      "Epoch: 15550, It: 15550, Lambda BCs: 6.377e+01, Lambda PDE: 9.922e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15600, It: 15600, Time: 1.25s, Learning Rate: 1.9e-05\n",
      "Epoch: 15600, It: 15600, Total Error: 2.263e+02, Total Loss: 7.982e-01\n",
      "Epoch: 15600, It: 15600, Relative L2: 1.494e-04, Relative Linfty: 4.041e-04\n",
      "Epoch: 15600, It: 15600, Loss_helmholtz: 2.263e+02, Loss BCs(u): 1.036e-02\n",
      "Epoch: 15600, It: 15600, Lambda BCs: 6.377e+01, Lambda PDE: 9.970e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 15650, It: 15650, Time: 1.24s, Learning Rate: 1.9e-05\n",
      "Epoch: 15650, It: 15650, Total Error: 2.256e+02, Total Loss: 7.956e-01\n",
      "Epoch: 15650, It: 15650, Relative L2: 1.522e-04, Relative Linfty: 4.632e-04\n",
      "Epoch: 15650, It: 15650, Loss_helmholtz: 2.256e+02, Loss BCs(u): 1.033e-02\n",
      "Epoch: 15650, It: 15650, Lambda BCs: 6.377e+01, Lambda PDE: 1.002e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15700, It: 15700, Time: 1.24s, Learning Rate: 1.8e-05\n",
      "Epoch: 15700, It: 15700, Total Error: 2.249e+02, Total Loss: 7.931e-01\n",
      "Epoch: 15700, It: 15700, Relative L2: 1.502e-04, Relative Linfty: 4.423e-04\n",
      "Epoch: 15700, It: 15700, Loss_helmholtz: 2.249e+02, Loss BCs(u): 1.029e-02\n",
      "Epoch: 15700, It: 15700, Lambda BCs: 6.377e+01, Lambda PDE: 1.006e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15750, It: 15750, Time: 1.24s, Learning Rate: 1.8e-05\n",
      "Epoch: 15750, It: 15750, Total Error: 2.242e+02, Total Loss: 7.906e-01\n",
      "Epoch: 15750, It: 15750, Relative L2: 1.455e-04, Relative Linfty: 3.965e-04\n",
      "Epoch: 15750, It: 15750, Loss_helmholtz: 2.242e+02, Loss BCs(u): 1.026e-02\n",
      "Epoch: 15750, It: 15750, Lambda BCs: 6.377e+01, Lambda PDE: 1.011e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15800, It: 15800, Time: 1.24s, Learning Rate: 1.8e-05\n",
      "Epoch: 15800, It: 15800, Total Error: 2.235e+02, Total Loss: 7.881e-01\n",
      "Epoch: 15800, It: 15800, Relative L2: 1.466e-04, Relative Linfty: 3.809e-04\n",
      "Epoch: 15800, It: 15800, Loss_helmholtz: 2.235e+02, Loss BCs(u): 1.023e-02\n",
      "Epoch: 15800, It: 15800, Lambda BCs: 6.377e+01, Lambda PDE: 1.015e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15850, It: 15850, Time: 1.24s, Learning Rate: 1.8e-05\n",
      "Epoch: 15850, It: 15850, Total Error: 2.228e+02, Total Loss: 7.856e-01\n",
      "Epoch: 15850, It: 15850, Relative L2: 1.433e-04, Relative Linfty: 4.103e-04\n",
      "Epoch: 15850, It: 15850, Loss_helmholtz: 2.228e+02, Loss BCs(u): 1.020e-02\n",
      "Epoch: 15850, It: 15850, Lambda BCs: 6.377e+01, Lambda PDE: 1.020e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15900, It: 15900, Time: 1.24s, Learning Rate: 1.7e-05\n",
      "Epoch: 15900, It: 15900, Total Error: 2.221e+02, Total Loss: 7.832e-01\n",
      "Epoch: 15900, It: 15900, Relative L2: 1.430e-04, Relative Linfty: 3.806e-04\n",
      "Epoch: 15900, It: 15900, Loss_helmholtz: 2.221e+02, Loss BCs(u): 1.017e-02\n",
      "Epoch: 15900, It: 15900, Lambda BCs: 6.377e+01, Lambda PDE: 1.024e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15950, It: 15950, Time: 1.24s, Learning Rate: 1.7e-05\n",
      "Epoch: 15950, It: 15950, Total Error: 2.214e+02, Total Loss: 7.807e-01\n",
      "Epoch: 15950, It: 15950, Relative L2: 1.473e-04, Relative Linfty: 3.815e-04\n",
      "Epoch: 15950, It: 15950, Loss_helmholtz: 2.214e+02, Loss BCs(u): 1.013e-02\n",
      "Epoch: 15950, It: 15950, Lambda BCs: 6.377e+01, Lambda PDE: 1.029e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16000, It: 16000, Time: 1.24s, Learning Rate: 1.7e-05\n",
      "Epoch: 16000, It: 16000, Total Error: 2.207e+02, Total Loss: 7.783e-01\n",
      "Epoch: 16000, It: 16000, Relative L2: 1.465e-04, Relative Linfty: 3.795e-04\n",
      "Epoch: 16000, It: 16000, Loss_helmholtz: 2.207e+02, Loss BCs(u): 1.010e-02\n",
      "Epoch: 16000, It: 16000, Lambda BCs: 6.377e+01, Lambda PDE: 1.034e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16050, It: 16050, Time: 1.24s, Learning Rate: 1.6e-05\n",
      "Epoch: 16050, It: 16050, Total Error: 2.200e+02, Total Loss: 7.759e-01\n",
      "Epoch: 16050, It: 16050, Relative L2: 1.513e-04, Relative Linfty: 3.868e-04\n",
      "Epoch: 16050, It: 16050, Loss_helmholtz: 2.200e+02, Loss BCs(u): 1.007e-02\n",
      "Epoch: 16050, It: 16050, Lambda BCs: 6.377e+01, Lambda PDE: 1.039e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16100, It: 16100, Time: 1.24s, Learning Rate: 1.6e-05\n",
      "Epoch: 16100, It: 16100, Total Error: 2.193e+02, Total Loss: 7.735e-01\n",
      "Epoch: 16100, It: 16100, Relative L2: 1.407e-04, Relative Linfty: 3.989e-04\n",
      "Epoch: 16100, It: 16100, Loss_helmholtz: 2.193e+02, Loss BCs(u): 1.004e-02\n",
      "Epoch: 16100, It: 16100, Lambda BCs: 6.377e+01, Lambda PDE: 1.043e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16150, It: 16150, Time: 1.24s, Learning Rate: 1.6e-05\n",
      "Epoch: 16150, It: 16150, Total Error: 2.187e+02, Total Loss: 7.711e-01\n",
      "Epoch: 16150, It: 16150, Relative L2: 1.455e-04, Relative Linfty: 4.047e-04\n",
      "Epoch: 16150, It: 16150, Loss_helmholtz: 2.186e+02, Loss BCs(u): 1.001e-02\n",
      "Epoch: 16150, It: 16150, Lambda BCs: 6.377e+01, Lambda PDE: 1.048e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16200, It: 16200, Time: 1.24s, Learning Rate: 1.5e-05\n",
      "Epoch: 16200, It: 16200, Total Error: 2.180e+02, Total Loss: 7.687e-01\n",
      "Epoch: 16200, It: 16200, Relative L2: 1.455e-04, Relative Linfty: 4.261e-04\n",
      "Epoch: 16200, It: 16200, Loss_helmholtz: 2.180e+02, Loss BCs(u): 9.978e-03\n",
      "Epoch: 16200, It: 16200, Lambda BCs: 6.377e+01, Lambda PDE: 1.053e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16250, It: 16250, Time: 1.24s, Learning Rate: 1.5e-05\n",
      "Epoch: 16250, It: 16250, Total Error: 2.173e+02, Total Loss: 7.664e-01\n",
      "Epoch: 16250, It: 16250, Relative L2: 1.404e-04, Relative Linfty: 3.997e-04\n",
      "Epoch: 16250, It: 16250, Loss_helmholtz: 2.173e+02, Loss BCs(u): 9.947e-03\n",
      "Epoch: 16250, It: 16250, Lambda BCs: 6.377e+01, Lambda PDE: 1.057e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16300, It: 16300, Time: 1.24s, Learning Rate: 1.5e-05\n",
      "Epoch: 16300, It: 16300, Total Error: 2.167e+02, Total Loss: 7.640e-01\n",
      "Epoch: 16300, It: 16300, Relative L2: 1.462e-04, Relative Linfty: 4.165e-04\n",
      "Epoch: 16300, It: 16300, Loss_helmholtz: 2.166e+02, Loss BCs(u): 9.917e-03\n",
      "Epoch: 16300, It: 16300, Lambda BCs: 6.377e+01, Lambda PDE: 1.062e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16350, It: 16350, Time: 1.24s, Learning Rate: 1.5e-05\n",
      "Epoch: 16350, It: 16350, Total Error: 2.160e+02, Total Loss: 7.617e-01\n",
      "Epoch: 16350, It: 16350, Relative L2: 1.474e-04, Relative Linfty: 4.411e-04\n",
      "Epoch: 16350, It: 16350, Loss_helmholtz: 2.160e+02, Loss BCs(u): 9.887e-03\n",
      "Epoch: 16350, It: 16350, Lambda BCs: 6.377e+01, Lambda PDE: 1.066e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16400, It: 16400, Time: 1.24s, Learning Rate: 1.4e-05\n",
      "Epoch: 16400, It: 16400, Total Error: 2.153e+02, Total Loss: 7.594e-01\n",
      "Epoch: 16400, It: 16400, Relative L2: 1.525e-04, Relative Linfty: 4.499e-04\n",
      "Epoch: 16400, It: 16400, Loss_helmholtz: 2.153e+02, Loss BCs(u): 9.857e-03\n",
      "Epoch: 16400, It: 16400, Lambda BCs: 6.377e+01, Lambda PDE: 1.071e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16450, It: 16450, Time: 1.24s, Learning Rate: 1.4e-05\n",
      "Epoch: 16450, It: 16450, Total Error: 2.147e+02, Total Loss: 7.571e-01\n",
      "Epoch: 16450, It: 16450, Relative L2: 1.507e-04, Relative Linfty: 4.080e-04\n",
      "Epoch: 16450, It: 16450, Loss_helmholtz: 2.147e+02, Loss BCs(u): 9.827e-03\n",
      "Epoch: 16450, It: 16450, Lambda BCs: 6.377e+01, Lambda PDE: 1.075e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16500, It: 16500, Time: 1.25s, Learning Rate: 1.4e-05\n",
      "Epoch: 16500, It: 16500, Total Error: 2.140e+02, Total Loss: 7.548e-01\n",
      "Epoch: 16500, It: 16500, Relative L2: 1.518e-04, Relative Linfty: 4.009e-04\n",
      "Epoch: 16500, It: 16500, Loss_helmholtz: 2.140e+02, Loss BCs(u): 9.797e-03\n",
      "Epoch: 16500, It: 16500, Lambda BCs: 6.377e+01, Lambda PDE: 1.080e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16550, It: 16550, Time: 1.24s, Learning Rate: 1.4e-05\n",
      "Epoch: 16550, It: 16550, Total Error: 2.134e+02, Total Loss: 7.525e-01\n",
      "Epoch: 16550, It: 16550, Relative L2: 1.490e-04, Relative Linfty: 4.575e-04\n",
      "Epoch: 16550, It: 16550, Loss_helmholtz: 2.134e+02, Loss BCs(u): 9.767e-03\n",
      "Epoch: 16550, It: 16550, Lambda BCs: 6.377e+01, Lambda PDE: 1.084e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16600, It: 16600, Time: 1.24s, Learning Rate: 1.3e-05\n",
      "Epoch: 16600, It: 16600, Total Error: 2.127e+02, Total Loss: 7.503e-01\n",
      "Epoch: 16600, It: 16600, Relative L2: 1.489e-04, Relative Linfty: 4.415e-04\n",
      "Epoch: 16600, It: 16600, Loss_helmholtz: 2.127e+02, Loss BCs(u): 9.738e-03\n",
      "Epoch: 16600, It: 16600, Lambda BCs: 6.377e+01, Lambda PDE: 1.088e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16650, It: 16650, Time: 1.24s, Learning Rate: 1.3e-05\n",
      "Epoch: 16650, It: 16650, Total Error: 2.121e+02, Total Loss: 7.480e-01\n",
      "Epoch: 16650, It: 16650, Relative L2: 1.439e-04, Relative Linfty: 4.423e-04\n",
      "Epoch: 16650, It: 16650, Loss_helmholtz: 2.121e+02, Loss BCs(u): 9.709e-03\n",
      "Epoch: 16650, It: 16650, Lambda BCs: 6.377e+01, Lambda PDE: 1.093e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16700, It: 16700, Time: 1.24s, Learning Rate: 1.3e-05\n",
      "Epoch: 16700, It: 16700, Total Error: 2.115e+02, Total Loss: 7.458e-01\n",
      "Epoch: 16700, It: 16700, Relative L2: 1.477e-04, Relative Linfty: 4.073e-04\n",
      "Epoch: 16700, It: 16700, Loss_helmholtz: 2.115e+02, Loss BCs(u): 9.680e-03\n",
      "Epoch: 16700, It: 16700, Lambda BCs: 6.377e+01, Lambda PDE: 1.098e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16750, It: 16750, Time: 1.24s, Learning Rate: 1.3e-05\n",
      "Epoch: 16750, It: 16750, Total Error: 2.108e+02, Total Loss: 7.436e-01\n",
      "Epoch: 16750, It: 16750, Relative L2: 1.365e-04, Relative Linfty: 3.794e-04\n",
      "Epoch: 16750, It: 16750, Loss_helmholtz: 2.108e+02, Loss BCs(u): 9.651e-03\n",
      "Epoch: 16750, It: 16750, Lambda BCs: 6.377e+01, Lambda PDE: 1.102e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16800, It: 16800, Time: 1.24s, Learning Rate: 1.2e-05\n",
      "Epoch: 16800, It: 16800, Total Error: 2.102e+02, Total Loss: 7.414e-01\n",
      "Epoch: 16800, It: 16800, Relative L2: 1.415e-04, Relative Linfty: 3.785e-04\n",
      "Epoch: 16800, It: 16800, Loss_helmholtz: 2.102e+02, Loss BCs(u): 9.623e-03\n",
      "Epoch: 16800, It: 16800, Lambda BCs: 6.377e+01, Lambda PDE: 1.106e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16850, It: 16850, Time: 1.24s, Learning Rate: 1.2e-05\n",
      "Epoch: 16850, It: 16850, Total Error: 2.096e+02, Total Loss: 7.392e-01\n",
      "Epoch: 16850, It: 16850, Relative L2: 1.416e-04, Relative Linfty: 4.609e-04\n",
      "Epoch: 16850, It: 16850, Loss_helmholtz: 2.096e+02, Loss BCs(u): 9.594e-03\n",
      "Epoch: 16850, It: 16850, Lambda BCs: 6.377e+01, Lambda PDE: 1.111e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16900, It: 16900, Time: 1.24s, Learning Rate: 1.2e-05\n",
      "Epoch: 16900, It: 16900, Total Error: 2.090e+02, Total Loss: 7.370e-01\n",
      "Epoch: 16900, It: 16900, Relative L2: 1.372e-04, Relative Linfty: 3.546e-04\n",
      "Epoch: 16900, It: 16900, Loss_helmholtz: 2.090e+02, Loss BCs(u): 9.566e-03\n",
      "Epoch: 16900, It: 16900, Lambda BCs: 6.377e+01, Lambda PDE: 1.115e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16950, It: 16950, Time: 1.24s, Learning Rate: 1.2e-05\n",
      "Epoch: 16950, It: 16950, Total Error: 2.084e+02, Total Loss: 7.348e-01\n",
      "Epoch: 16950, It: 16950, Relative L2: 1.439e-04, Relative Linfty: 3.784e-04\n",
      "Epoch: 16950, It: 16950, Loss_helmholtz: 2.084e+02, Loss BCs(u): 9.538e-03\n",
      "Epoch: 16950, It: 16950, Lambda BCs: 6.377e+01, Lambda PDE: 1.119e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17000, It: 17000, Time: 1.24s, Learning Rate: 1.2e-05\n",
      "Epoch: 17000, It: 17000, Total Error: 2.078e+02, Total Loss: 7.327e-01\n",
      "Epoch: 17000, It: 17000, Relative L2: 1.439e-04, Relative Linfty: 3.730e-04\n",
      "Epoch: 17000, It: 17000, Loss_helmholtz: 2.077e+02, Loss BCs(u): 9.510e-03\n",
      "Epoch: 17000, It: 17000, Lambda BCs: 6.377e+01, Lambda PDE: 1.123e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17050, It: 17050, Time: 1.24s, Learning Rate: 1.1e-05\n",
      "Epoch: 17050, It: 17050, Total Error: 2.071e+02, Total Loss: 7.306e-01\n",
      "Epoch: 17050, It: 17050, Relative L2: 1.409e-04, Relative Linfty: 3.853e-04\n",
      "Epoch: 17050, It: 17050, Loss_helmholtz: 2.071e+02, Loss BCs(u): 9.482e-03\n",
      "Epoch: 17050, It: 17050, Lambda BCs: 6.377e+01, Lambda PDE: 1.128e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17100, It: 17100, Time: 1.24s, Learning Rate: 1.1e-05\n",
      "Epoch: 17100, It: 17100, Total Error: 2.065e+02, Total Loss: 7.284e-01\n",
      "Epoch: 17100, It: 17100, Relative L2: 1.498e-04, Relative Linfty: 3.906e-04\n",
      "Epoch: 17100, It: 17100, Loss_helmholtz: 2.065e+02, Loss BCs(u): 9.454e-03\n",
      "Epoch: 17100, It: 17100, Lambda BCs: 6.377e+01, Lambda PDE: 1.132e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17150, It: 17150, Time: 1.24s, Learning Rate: 1.1e-05\n",
      "Epoch: 17150, It: 17150, Total Error: 2.059e+02, Total Loss: 7.263e-01\n",
      "Epoch: 17150, It: 17150, Relative L2: 1.430e-04, Relative Linfty: 4.130e-04\n",
      "Epoch: 17150, It: 17150, Loss_helmholtz: 2.059e+02, Loss BCs(u): 9.427e-03\n",
      "Epoch: 17150, It: 17150, Lambda BCs: 6.377e+01, Lambda PDE: 1.136e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17200, It: 17200, Time: 1.24s, Learning Rate: 1.1e-05\n",
      "Epoch: 17200, It: 17200, Total Error: 2.053e+02, Total Loss: 7.242e-01\n",
      "Epoch: 17200, It: 17200, Relative L2: 1.438e-04, Relative Linfty: 4.148e-04\n",
      "Epoch: 17200, It: 17200, Loss_helmholtz: 2.053e+02, Loss BCs(u): 9.399e-03\n",
      "Epoch: 17200, It: 17200, Lambda BCs: 6.377e+01, Lambda PDE: 1.140e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17250, It: 17250, Time: 1.24s, Learning Rate: 1.1e-05\n",
      "Epoch: 17250, It: 17250, Total Error: 2.048e+02, Total Loss: 7.221e-01\n",
      "Epoch: 17250, It: 17250, Relative L2: 1.470e-04, Relative Linfty: 4.014e-04\n",
      "Epoch: 17250, It: 17250, Loss_helmholtz: 2.047e+02, Loss BCs(u): 9.372e-03\n",
      "Epoch: 17250, It: 17250, Lambda BCs: 6.377e+01, Lambda PDE: 1.145e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17300, It: 17300, Time: 1.27s, Learning Rate: 1.0e-05\n",
      "Epoch: 17300, It: 17300, Total Error: 2.042e+02, Total Loss: 7.200e-01\n",
      "Epoch: 17300, It: 17300, Relative L2: 1.420e-04, Relative Linfty: 3.562e-04\n",
      "Epoch: 17300, It: 17300, Loss_helmholtz: 2.042e+02, Loss BCs(u): 9.345e-03\n",
      "Epoch: 17300, It: 17300, Lambda BCs: 6.377e+01, Lambda PDE: 1.149e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17350, It: 17350, Time: 1.24s, Learning Rate: 1.0e-05\n",
      "Epoch: 17350, It: 17350, Total Error: 2.036e+02, Total Loss: 7.180e-01\n",
      "Epoch: 17350, It: 17350, Relative L2: 1.394e-04, Relative Linfty: 3.610e-04\n",
      "Epoch: 17350, It: 17350, Loss_helmholtz: 2.036e+02, Loss BCs(u): 9.318e-03\n",
      "Epoch: 17350, It: 17350, Lambda BCs: 6.377e+01, Lambda PDE: 1.153e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17400, It: 17400, Time: 1.25s, Learning Rate: 1.0e-05\n",
      "Epoch: 17400, It: 17400, Total Error: 2.030e+02, Total Loss: 7.159e-01\n",
      "Epoch: 17400, It: 17400, Relative L2: 1.500e-04, Relative Linfty: 3.782e-04\n",
      "Epoch: 17400, It: 17400, Loss_helmholtz: 2.030e+02, Loss BCs(u): 9.292e-03\n",
      "Epoch: 17400, It: 17400, Lambda BCs: 6.377e+01, Lambda PDE: 1.157e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17450, It: 17450, Time: 1.25s, Learning Rate: 9.9e-06\n",
      "Epoch: 17450, It: 17450, Total Error: 2.024e+02, Total Loss: 7.139e-01\n",
      "Epoch: 17450, It: 17450, Relative L2: 1.456e-04, Relative Linfty: 4.005e-04\n",
      "Epoch: 17450, It: 17450, Loss_helmholtz: 2.024e+02, Loss BCs(u): 9.265e-03\n",
      "Epoch: 17450, It: 17450, Lambda BCs: 6.377e+01, Lambda PDE: 1.161e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17500, It: 17500, Time: 1.24s, Learning Rate: 9.7e-06\n",
      "Epoch: 17500, It: 17500, Total Error: 2.018e+02, Total Loss: 7.118e-01\n",
      "Epoch: 17500, It: 17500, Relative L2: 1.516e-04, Relative Linfty: 3.924e-04\n",
      "Epoch: 17500, It: 17500, Loss_helmholtz: 2.018e+02, Loss BCs(u): 9.239e-03\n",
      "Epoch: 17500, It: 17500, Lambda BCs: 6.377e+01, Lambda PDE: 1.165e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17550, It: 17550, Time: 1.24s, Learning Rate: 9.6e-06\n",
      "Epoch: 17550, It: 17550, Total Error: 2.013e+02, Total Loss: 7.098e-01\n",
      "Epoch: 17550, It: 17550, Relative L2: 1.477e-04, Relative Linfty: 4.290e-04\n",
      "Epoch: 17550, It: 17550, Loss_helmholtz: 2.013e+02, Loss BCs(u): 9.212e-03\n",
      "Epoch: 17550, It: 17550, Lambda BCs: 6.377e+01, Lambda PDE: 1.169e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17600, It: 17600, Time: 1.24s, Learning Rate: 9.4e-06\n",
      "Epoch: 17600, It: 17600, Total Error: 2.007e+02, Total Loss: 7.078e-01\n",
      "Epoch: 17600, It: 17600, Relative L2: 1.583e-04, Relative Linfty: 4.302e-04\n",
      "Epoch: 17600, It: 17600, Loss_helmholtz: 2.007e+02, Loss BCs(u): 9.186e-03\n",
      "Epoch: 17600, It: 17600, Lambda BCs: 6.377e+01, Lambda PDE: 1.173e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17650, It: 17650, Time: 1.26s, Learning Rate: 9.2e-06\n",
      "Epoch: 17650, It: 17650, Total Error: 2.001e+02, Total Loss: 7.058e-01\n",
      "Epoch: 17650, It: 17650, Relative L2: 1.480e-04, Relative Linfty: 4.170e-04\n",
      "Epoch: 17650, It: 17650, Loss_helmholtz: 2.001e+02, Loss BCs(u): 9.160e-03\n",
      "Epoch: 17650, It: 17650, Lambda BCs: 6.377e+01, Lambda PDE: 1.178e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17700, It: 17700, Time: 1.24s, Learning Rate: 9.1e-06\n",
      "Epoch: 17700, It: 17700, Total Error: 1.996e+02, Total Loss: 7.038e-01\n",
      "Epoch: 17700, It: 17700, Relative L2: 1.500e-04, Relative Linfty: 4.325e-04\n",
      "Epoch: 17700, It: 17700, Loss_helmholtz: 1.996e+02, Loss BCs(u): 9.135e-03\n",
      "Epoch: 17700, It: 17700, Lambda BCs: 6.377e+01, Lambda PDE: 1.182e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17750, It: 17750, Time: 1.24s, Learning Rate: 8.9e-06\n",
      "Epoch: 17750, It: 17750, Total Error: 1.990e+02, Total Loss: 7.019e-01\n",
      "Epoch: 17750, It: 17750, Relative L2: 1.428e-04, Relative Linfty: 4.162e-04\n",
      "Epoch: 17750, It: 17750, Loss_helmholtz: 1.990e+02, Loss BCs(u): 9.109e-03\n",
      "Epoch: 17750, It: 17750, Lambda BCs: 6.377e+01, Lambda PDE: 1.186e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17800, It: 17800, Time: 1.24s, Learning Rate: 8.7e-06\n",
      "Epoch: 17800, It: 17800, Total Error: 1.984e+02, Total Loss: 6.999e-01\n",
      "Epoch: 17800, It: 17800, Relative L2: 1.463e-04, Relative Linfty: 4.209e-04\n",
      "Epoch: 17800, It: 17800, Loss_helmholtz: 1.984e+02, Loss BCs(u): 9.083e-03\n",
      "Epoch: 17800, It: 17800, Lambda BCs: 6.377e+01, Lambda PDE: 1.190e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17850, It: 17850, Time: 1.24s, Learning Rate: 8.6e-06\n",
      "Epoch: 17850, It: 17850, Total Error: 1.979e+02, Total Loss: 6.979e-01\n",
      "Epoch: 17850, It: 17850, Relative L2: 1.439e-04, Relative Linfty: 4.102e-04\n",
      "Epoch: 17850, It: 17850, Loss_helmholtz: 1.979e+02, Loss BCs(u): 9.058e-03\n",
      "Epoch: 17850, It: 17850, Lambda BCs: 6.377e+01, Lambda PDE: 1.194e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17900, It: 17900, Time: 1.24s, Learning Rate: 8.4e-06\n",
      "Epoch: 17900, It: 17900, Total Error: 1.973e+02, Total Loss: 6.960e-01\n",
      "Epoch: 17900, It: 17900, Relative L2: 1.394e-04, Relative Linfty: 4.120e-04\n",
      "Epoch: 17900, It: 17900, Loss_helmholtz: 1.973e+02, Loss BCs(u): 9.033e-03\n",
      "Epoch: 17900, It: 17900, Lambda BCs: 6.377e+01, Lambda PDE: 1.198e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17950, It: 17950, Time: 1.24s, Learning Rate: 8.3e-06\n",
      "Epoch: 17950, It: 17950, Total Error: 1.968e+02, Total Loss: 6.941e-01\n",
      "Epoch: 17950, It: 17950, Relative L2: 1.462e-04, Relative Linfty: 3.914e-04\n",
      "Epoch: 17950, It: 17950, Loss_helmholtz: 1.968e+02, Loss BCs(u): 9.008e-03\n",
      "Epoch: 17950, It: 17950, Lambda BCs: 6.377e+01, Lambda PDE: 1.202e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18000, It: 18000, Time: 1.25s, Learning Rate: 8.1e-06\n",
      "Epoch: 18000, It: 18000, Total Error: 1.962e+02, Total Loss: 6.921e-01\n",
      "Epoch: 18000, It: 18000, Relative L2: 1.373e-04, Relative Linfty: 4.134e-04\n",
      "Epoch: 18000, It: 18000, Loss_helmholtz: 1.962e+02, Loss BCs(u): 8.983e-03\n",
      "Epoch: 18000, It: 18000, Lambda BCs: 6.377e+01, Lambda PDE: 1.206e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18050, It: 18050, Time: 1.24s, Learning Rate: 8.0e-06\n",
      "Epoch: 18050, It: 18050, Total Error: 1.957e+02, Total Loss: 6.902e-01\n",
      "Epoch: 18050, It: 18050, Relative L2: 1.411e-04, Relative Linfty: 4.158e-04\n",
      "Epoch: 18050, It: 18050, Loss_helmholtz: 1.957e+02, Loss BCs(u): 8.958e-03\n",
      "Epoch: 18050, It: 18050, Lambda BCs: 6.377e+01, Lambda PDE: 1.210e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18100, It: 18100, Time: 1.24s, Learning Rate: 7.9e-06\n",
      "Epoch: 18100, It: 18100, Total Error: 1.952e+02, Total Loss: 6.883e-01\n",
      "Epoch: 18100, It: 18100, Relative L2: 1.378e-04, Relative Linfty: 4.229e-04\n",
      "Epoch: 18100, It: 18100, Loss_helmholtz: 1.952e+02, Loss BCs(u): 8.933e-03\n",
      "Epoch: 18100, It: 18100, Lambda BCs: 6.377e+01, Lambda PDE: 1.214e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18150, It: 18150, Time: 1.24s, Learning Rate: 7.7e-06\n",
      "Epoch: 18150, It: 18150, Total Error: 1.946e+02, Total Loss: 6.864e-01\n",
      "Epoch: 18150, It: 18150, Relative L2: 1.430e-04, Relative Linfty: 4.005e-04\n",
      "Epoch: 18150, It: 18150, Loss_helmholtz: 1.946e+02, Loss BCs(u): 8.909e-03\n",
      "Epoch: 18150, It: 18150, Lambda BCs: 6.377e+01, Lambda PDE: 1.217e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18200, It: 18200, Time: 1.24s, Learning Rate: 7.6e-06\n",
      "Epoch: 18200, It: 18200, Total Error: 1.941e+02, Total Loss: 6.846e-01\n",
      "Epoch: 18200, It: 18200, Relative L2: 1.384e-04, Relative Linfty: 3.791e-04\n",
      "Epoch: 18200, It: 18200, Loss_helmholtz: 1.941e+02, Loss BCs(u): 8.884e-03\n",
      "Epoch: 18200, It: 18200, Lambda BCs: 6.377e+01, Lambda PDE: 1.221e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18250, It: 18250, Time: 1.24s, Learning Rate: 7.4e-06\n",
      "Epoch: 18250, It: 18250, Total Error: 1.936e+02, Total Loss: 6.827e-01\n",
      "Epoch: 18250, It: 18250, Relative L2: 1.377e-04, Relative Linfty: 4.264e-04\n",
      "Epoch: 18250, It: 18250, Loss_helmholtz: 1.936e+02, Loss BCs(u): 8.860e-03\n",
      "Epoch: 18250, It: 18250, Lambda BCs: 6.377e+01, Lambda PDE: 1.225e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18300, It: 18300, Time: 1.26s, Learning Rate: 7.3e-06\n",
      "Epoch: 18300, It: 18300, Total Error: 1.930e+02, Total Loss: 6.808e-01\n",
      "Epoch: 18300, It: 18300, Relative L2: 1.353e-04, Relative Linfty: 3.702e-04\n",
      "Epoch: 18300, It: 18300, Loss_helmholtz: 1.930e+02, Loss BCs(u): 8.836e-03\n",
      "Epoch: 18300, It: 18300, Lambda BCs: 6.377e+01, Lambda PDE: 1.229e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18350, It: 18350, Time: 1.24s, Learning Rate: 7.2e-06\n",
      "Epoch: 18350, It: 18350, Total Error: 1.925e+02, Total Loss: 6.790e-01\n",
      "Epoch: 18350, It: 18350, Relative L2: 1.339e-04, Relative Linfty: 4.013e-04\n",
      "Epoch: 18350, It: 18350, Loss_helmholtz: 1.925e+02, Loss BCs(u): 8.812e-03\n",
      "Epoch: 18350, It: 18350, Lambda BCs: 6.377e+01, Lambda PDE: 1.233e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18400, It: 18400, Time: 1.24s, Learning Rate: 7.1e-06\n",
      "Epoch: 18400, It: 18400, Total Error: 1.920e+02, Total Loss: 6.772e-01\n",
      "Epoch: 18400, It: 18400, Relative L2: 1.339e-04, Relative Linfty: 3.670e-04\n",
      "Epoch: 18400, It: 18400, Loss_helmholtz: 1.920e+02, Loss BCs(u): 8.788e-03\n",
      "Epoch: 18400, It: 18400, Lambda BCs: 6.377e+01, Lambda PDE: 1.237e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18450, It: 18450, Time: 1.24s, Learning Rate: 6.9e-06\n",
      "Epoch: 18450, It: 18450, Total Error: 1.915e+02, Total Loss: 6.753e-01\n",
      "Epoch: 18450, It: 18450, Relative L2: 1.383e-04, Relative Linfty: 4.025e-04\n",
      "Epoch: 18450, It: 18450, Loss_helmholtz: 1.915e+02, Loss BCs(u): 8.764e-03\n",
      "Epoch: 18450, It: 18450, Lambda BCs: 6.377e+01, Lambda PDE: 1.241e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18500, It: 18500, Time: 1.24s, Learning Rate: 6.8e-06\n",
      "Epoch: 18500, It: 18500, Total Error: 1.910e+02, Total Loss: 6.735e-01\n",
      "Epoch: 18500, It: 18500, Relative L2: 1.362e-04, Relative Linfty: 3.521e-04\n",
      "Epoch: 18500, It: 18500, Loss_helmholtz: 1.909e+02, Loss BCs(u): 8.741e-03\n",
      "Epoch: 18500, It: 18500, Lambda BCs: 6.377e+01, Lambda PDE: 1.245e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18550, It: 18550, Time: 1.24s, Learning Rate: 6.7e-06\n",
      "Epoch: 18550, It: 18550, Total Error: 1.904e+02, Total Loss: 6.717e-01\n",
      "Epoch: 18550, It: 18550, Relative L2: 1.328e-04, Relative Linfty: 3.507e-04\n",
      "Epoch: 18550, It: 18550, Loss_helmholtz: 1.904e+02, Loss BCs(u): 8.717e-03\n",
      "Epoch: 18550, It: 18550, Lambda BCs: 6.377e+01, Lambda PDE: 1.249e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18600, It: 18600, Time: 1.24s, Learning Rate: 6.6e-06\n",
      "Epoch: 18600, It: 18600, Total Error: 1.899e+02, Total Loss: 6.699e-01\n",
      "Epoch: 18600, It: 18600, Relative L2: 1.285e-04, Relative Linfty: 3.758e-04\n",
      "Epoch: 18600, It: 18600, Loss_helmholtz: 1.899e+02, Loss BCs(u): 8.694e-03\n",
      "Epoch: 18600, It: 18600, Lambda BCs: 6.377e+01, Lambda PDE: 1.252e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18650, It: 18650, Time: 1.24s, Learning Rate: 6.5e-06\n",
      "Epoch: 18650, It: 18650, Total Error: 1.894e+02, Total Loss: 6.681e-01\n",
      "Epoch: 18650, It: 18650, Relative L2: 1.312e-04, Relative Linfty: 3.801e-04\n",
      "Epoch: 18650, It: 18650, Loss_helmholtz: 1.894e+02, Loss BCs(u): 8.671e-03\n",
      "Epoch: 18650, It: 18650, Lambda BCs: 6.377e+01, Lambda PDE: 1.256e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18700, It: 18700, Time: 1.24s, Learning Rate: 6.3e-06\n",
      "Epoch: 18700, It: 18700, Total Error: 1.889e+02, Total Loss: 6.663e-01\n",
      "Epoch: 18700, It: 18700, Relative L2: 1.318e-04, Relative Linfty: 3.711e-04\n",
      "Epoch: 18700, It: 18700, Loss_helmholtz: 1.889e+02, Loss BCs(u): 8.647e-03\n",
      "Epoch: 18700, It: 18700, Lambda BCs: 6.377e+01, Lambda PDE: 1.260e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18750, It: 18750, Time: 1.24s, Learning Rate: 6.2e-06\n",
      "Epoch: 18750, It: 18750, Total Error: 1.884e+02, Total Loss: 6.646e-01\n",
      "Epoch: 18750, It: 18750, Relative L2: 1.358e-04, Relative Linfty: 3.809e-04\n",
      "Epoch: 18750, It: 18750, Loss_helmholtz: 1.884e+02, Loss BCs(u): 8.624e-03\n",
      "Epoch: 18750, It: 18750, Lambda BCs: 6.377e+01, Lambda PDE: 1.264e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18800, It: 18800, Time: 1.24s, Learning Rate: 6.1e-06\n",
      "Epoch: 18800, It: 18800, Total Error: 1.879e+02, Total Loss: 6.628e-01\n",
      "Epoch: 18800, It: 18800, Relative L2: 1.317e-04, Relative Linfty: 3.732e-04\n",
      "Epoch: 18800, It: 18800, Loss_helmholtz: 1.879e+02, Loss BCs(u): 8.602e-03\n",
      "Epoch: 18800, It: 18800, Lambda BCs: 6.377e+01, Lambda PDE: 1.268e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18850, It: 18850, Time: 1.29s, Learning Rate: 6.0e-06\n",
      "Epoch: 18850, It: 18850, Total Error: 1.874e+02, Total Loss: 6.610e-01\n",
      "Epoch: 18850, It: 18850, Relative L2: 1.374e-04, Relative Linfty: 3.696e-04\n",
      "Epoch: 18850, It: 18850, Loss_helmholtz: 1.874e+02, Loss BCs(u): 8.579e-03\n",
      "Epoch: 18850, It: 18850, Lambda BCs: 6.377e+01, Lambda PDE: 1.272e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18900, It: 18900, Time: 1.24s, Learning Rate: 5.9e-06\n",
      "Epoch: 18900, It: 18900, Total Error: 1.869e+02, Total Loss: 6.593e-01\n",
      "Epoch: 18900, It: 18900, Relative L2: 1.421e-04, Relative Linfty: 3.960e-04\n",
      "Epoch: 18900, It: 18900, Loss_helmholtz: 1.869e+02, Loss BCs(u): 8.556e-03\n",
      "Epoch: 18900, It: 18900, Lambda BCs: 6.377e+01, Lambda PDE: 1.276e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18950, It: 18950, Time: 1.24s, Learning Rate: 5.8e-06\n",
      "Epoch: 18950, It: 18950, Total Error: 1.864e+02, Total Loss: 6.576e-01\n",
      "Epoch: 18950, It: 18950, Relative L2: 1.337e-04, Relative Linfty: 3.975e-04\n",
      "Epoch: 18950, It: 18950, Loss_helmholtz: 1.864e+02, Loss BCs(u): 8.534e-03\n",
      "Epoch: 18950, It: 18950, Lambda BCs: 6.377e+01, Lambda PDE: 1.280e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19000, It: 19000, Time: 1.24s, Learning Rate: 5.7e-06\n",
      "Epoch: 19000, It: 19000, Total Error: 1.859e+02, Total Loss: 6.558e-01\n",
      "Epoch: 19000, It: 19000, Relative L2: 1.372e-04, Relative Linfty: 4.015e-04\n",
      "Epoch: 19000, It: 19000, Loss_helmholtz: 1.859e+02, Loss BCs(u): 8.511e-03\n",
      "Epoch: 19000, It: 19000, Lambda BCs: 6.377e+01, Lambda PDE: 1.283e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19050, It: 19050, Time: 1.24s, Learning Rate: 5.6e-06\n",
      "Epoch: 19050, It: 19050, Total Error: 1.855e+02, Total Loss: 6.541e-01\n",
      "Epoch: 19050, It: 19050, Relative L2: 1.326e-04, Relative Linfty: 3.877e-04\n",
      "Epoch: 19050, It: 19050, Loss_helmholtz: 1.854e+02, Loss BCs(u): 8.489e-03\n",
      "Epoch: 19050, It: 19050, Lambda BCs: 6.377e+01, Lambda PDE: 1.287e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19100, It: 19100, Time: 1.24s, Learning Rate: 5.5e-06\n",
      "Epoch: 19100, It: 19100, Total Error: 1.850e+02, Total Loss: 6.524e-01\n",
      "Epoch: 19100, It: 19100, Relative L2: 1.373e-04, Relative Linfty: 4.264e-04\n",
      "Epoch: 19100, It: 19100, Loss_helmholtz: 1.850e+02, Loss BCs(u): 8.467e-03\n",
      "Epoch: 19100, It: 19100, Lambda BCs: 6.377e+01, Lambda PDE: 1.291e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19150, It: 19150, Time: 1.24s, Learning Rate: 5.4e-06\n",
      "Epoch: 19150, It: 19150, Total Error: 1.845e+02, Total Loss: 6.507e-01\n",
      "Epoch: 19150, It: 19150, Relative L2: 1.308e-04, Relative Linfty: 3.617e-04\n",
      "Epoch: 19150, It: 19150, Loss_helmholtz: 1.845e+02, Loss BCs(u): 8.445e-03\n",
      "Epoch: 19150, It: 19150, Lambda BCs: 6.377e+01, Lambda PDE: 1.295e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19200, It: 19200, Time: 1.24s, Learning Rate: 5.3e-06\n",
      "Epoch: 19200, It: 19200, Total Error: 1.840e+02, Total Loss: 6.490e-01\n",
      "Epoch: 19200, It: 19200, Relative L2: 1.328e-04, Relative Linfty: 3.707e-04\n",
      "Epoch: 19200, It: 19200, Loss_helmholtz: 1.840e+02, Loss BCs(u): 8.423e-03\n",
      "Epoch: 19200, It: 19200, Lambda BCs: 6.377e+01, Lambda PDE: 1.299e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19250, It: 19250, Time: 1.24s, Learning Rate: 5.2e-06\n",
      "Epoch: 19250, It: 19250, Total Error: 1.835e+02, Total Loss: 6.474e-01\n",
      "Epoch: 19250, It: 19250, Relative L2: 1.399e-04, Relative Linfty: 3.592e-04\n",
      "Epoch: 19250, It: 19250, Loss_helmholtz: 1.835e+02, Loss BCs(u): 8.401e-03\n",
      "Epoch: 19250, It: 19250, Lambda BCs: 6.377e+01, Lambda PDE: 1.303e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19300, It: 19300, Time: 1.24s, Learning Rate: 5.1e-06\n",
      "Epoch: 19300, It: 19300, Total Error: 1.831e+02, Total Loss: 6.457e-01\n",
      "Epoch: 19300, It: 19300, Relative L2: 1.335e-04, Relative Linfty: 3.683e-04\n",
      "Epoch: 19300, It: 19300, Loss_helmholtz: 1.831e+02, Loss BCs(u): 8.379e-03\n",
      "Epoch: 19300, It: 19300, Lambda BCs: 6.377e+01, Lambda PDE: 1.307e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19350, It: 19350, Time: 1.24s, Learning Rate: 5.0e-06\n",
      "Epoch: 19350, It: 19350, Total Error: 1.826e+02, Total Loss: 6.440e-01\n",
      "Epoch: 19350, It: 19350, Relative L2: 1.443e-04, Relative Linfty: 3.647e-04\n",
      "Epoch: 19350, It: 19350, Loss_helmholtz: 1.826e+02, Loss BCs(u): 8.358e-03\n",
      "Epoch: 19350, It: 19350, Lambda BCs: 6.377e+01, Lambda PDE: 1.311e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19400, It: 19400, Time: 1.24s, Learning Rate: 4.9e-06\n",
      "Epoch: 19400, It: 19400, Total Error: 1.821e+02, Total Loss: 6.424e-01\n",
      "Epoch: 19400, It: 19400, Relative L2: 1.343e-04, Relative Linfty: 3.748e-04\n",
      "Epoch: 19400, It: 19400, Loss_helmholtz: 1.821e+02, Loss BCs(u): 8.336e-03\n",
      "Epoch: 19400, It: 19400, Lambda BCs: 6.377e+01, Lambda PDE: 1.315e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19450, It: 19450, Time: 1.24s, Learning Rate: 4.9e-06\n",
      "Epoch: 19450, It: 19450, Total Error: 1.817e+02, Total Loss: 6.407e-01\n",
      "Epoch: 19450, It: 19450, Relative L2: 1.350e-04, Relative Linfty: 3.511e-04\n",
      "Epoch: 19450, It: 19450, Loss_helmholtz: 1.816e+02, Loss BCs(u): 8.315e-03\n",
      "Epoch: 19450, It: 19450, Lambda BCs: 6.377e+01, Lambda PDE: 1.318e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19500, It: 19500, Time: 1.24s, Learning Rate: 4.8e-06\n",
      "Epoch: 19500, It: 19500, Total Error: 1.812e+02, Total Loss: 6.391e-01\n",
      "Epoch: 19500, It: 19500, Relative L2: 1.342e-04, Relative Linfty: 4.129e-04\n",
      "Epoch: 19500, It: 19500, Loss_helmholtz: 1.812e+02, Loss BCs(u): 8.294e-03\n",
      "Epoch: 19500, It: 19500, Lambda BCs: 6.377e+01, Lambda PDE: 1.322e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19550, It: 19550, Time: 1.24s, Learning Rate: 4.7e-06\n",
      "Epoch: 19550, It: 19550, Total Error: 1.807e+02, Total Loss: 6.375e-01\n",
      "Epoch: 19550, It: 19550, Relative L2: 1.311e-04, Relative Linfty: 3.736e-04\n",
      "Epoch: 19550, It: 19550, Loss_helmholtz: 1.807e+02, Loss BCs(u): 8.272e-03\n",
      "Epoch: 19550, It: 19550, Lambda BCs: 6.377e+01, Lambda PDE: 1.326e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19600, It: 19600, Time: 1.24s, Learning Rate: 4.6e-06\n",
      "Epoch: 19600, It: 19600, Total Error: 1.803e+02, Total Loss: 6.358e-01\n",
      "Epoch: 19600, It: 19600, Relative L2: 1.307e-04, Relative Linfty: 3.808e-04\n",
      "Epoch: 19600, It: 19600, Loss_helmholtz: 1.803e+02, Loss BCs(u): 8.251e-03\n",
      "Epoch: 19600, It: 19600, Lambda BCs: 6.377e+01, Lambda PDE: 1.330e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19650, It: 19650, Time: 1.24s, Learning Rate: 4.5e-06\n",
      "Epoch: 19650, It: 19650, Total Error: 1.798e+02, Total Loss: 6.342e-01\n",
      "Epoch: 19650, It: 19650, Relative L2: 1.340e-04, Relative Linfty: 3.619e-04\n",
      "Epoch: 19650, It: 19650, Loss_helmholtz: 1.798e+02, Loss BCs(u): 8.230e-03\n",
      "Epoch: 19650, It: 19650, Lambda BCs: 6.377e+01, Lambda PDE: 1.334e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19700, It: 19700, Time: 1.24s, Learning Rate: 4.4e-06\n",
      "Epoch: 19700, It: 19700, Total Error: 1.794e+02, Total Loss: 6.326e-01\n",
      "Epoch: 19700, It: 19700, Relative L2: 1.356e-04, Relative Linfty: 3.921e-04\n",
      "Epoch: 19700, It: 19700, Loss_helmholtz: 1.793e+02, Loss BCs(u): 8.210e-03\n",
      "Epoch: 19700, It: 19700, Lambda BCs: 6.377e+01, Lambda PDE: 1.337e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19750, It: 19750, Time: 1.24s, Learning Rate: 4.4e-06\n",
      "Epoch: 19750, It: 19750, Total Error: 1.789e+02, Total Loss: 6.310e-01\n",
      "Epoch: 19750, It: 19750, Relative L2: 1.370e-04, Relative Linfty: 3.639e-04\n",
      "Epoch: 19750, It: 19750, Loss_helmholtz: 1.789e+02, Loss BCs(u): 8.189e-03\n",
      "Epoch: 19750, It: 19750, Lambda BCs: 6.377e+01, Lambda PDE: 1.341e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19800, It: 19800, Time: 1.24s, Learning Rate: 4.3e-06\n",
      "Epoch: 19800, It: 19800, Total Error: 1.784e+02, Total Loss: 6.294e-01\n",
      "Epoch: 19800, It: 19800, Relative L2: 1.342e-04, Relative Linfty: 3.745e-04\n",
      "Epoch: 19800, It: 19800, Loss_helmholtz: 1.784e+02, Loss BCs(u): 8.168e-03\n",
      "Epoch: 19800, It: 19800, Lambda BCs: 6.377e+01, Lambda PDE: 1.345e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19850, It: 19850, Time: 1.24s, Learning Rate: 4.2e-06\n",
      "Epoch: 19850, It: 19850, Total Error: 1.780e+02, Total Loss: 6.279e-01\n",
      "Epoch: 19850, It: 19850, Relative L2: 1.317e-04, Relative Linfty: 3.662e-04\n",
      "Epoch: 19850, It: 19850, Loss_helmholtz: 1.780e+02, Loss BCs(u): 8.148e-03\n",
      "Epoch: 19850, It: 19850, Lambda BCs: 6.377e+01, Lambda PDE: 1.349e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19900, It: 19900, Time: 1.24s, Learning Rate: 4.1e-06\n",
      "Epoch: 19900, It: 19900, Total Error: 1.776e+02, Total Loss: 6.263e-01\n",
      "Epoch: 19900, It: 19900, Relative L2: 1.379e-04, Relative Linfty: 3.859e-04\n",
      "Epoch: 19900, It: 19900, Loss_helmholtz: 1.775e+02, Loss BCs(u): 8.127e-03\n",
      "Epoch: 19900, It: 19900, Lambda BCs: 6.377e+01, Lambda PDE: 1.352e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19950, It: 19950, Time: 1.24s, Learning Rate: 4.1e-06\n",
      "Epoch: 19950, It: 19950, Total Error: 1.771e+02, Total Loss: 6.247e-01\n",
      "Epoch: 19950, It: 19950, Relative L2: 1.422e-04, Relative Linfty: 3.780e-04\n",
      "Epoch: 19950, It: 19950, Loss_helmholtz: 1.771e+02, Loss BCs(u): 8.107e-03\n",
      "Epoch: 19950, It: 19950, Lambda BCs: 6.377e+01, Lambda PDE: 1.356e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20000, It: 20000, Time: 1.24s, Learning Rate: 4.0e-06\n",
      "Epoch: 20000, It: 20000, Total Error: 1.767e+02, Total Loss: 6.232e-01\n",
      "Epoch: 20000, It: 20000, Relative L2: 1.391e-04, Relative Linfty: 3.793e-04\n",
      "Epoch: 20000, It: 20000, Loss_helmholtz: 1.767e+02, Loss BCs(u): 8.087e-03\n",
      "Epoch: 20000, It: 20000, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n"
     ]
    }
   ],
   "source": [
    "log_loss     = []\n",
    "log_epoch    = []\n",
    "log_lambdas  = []\n",
    "epoch_losses = []\n",
    "log_u_pred   = []\n",
    "log_res      = []\n",
    "starting_epoch=0\n",
    "M1= Norm_metric1(X_res[:, :2])\n",
    "M2= Norm_metric2(X_res[:, :2])\n",
    "Errors=np.zeros((1,3))\n",
    "errors=np.zeros((1,3))\n",
    "start_time = time.time()\n",
    "start_time2 = time.time()\n",
    "for epoch in range(1, num_epochs_adam+1):\n",
    "    #idx_res=np.random.choice(len(X_res),int(len(X_res)*1)) \n",
    "    idx_res=np.arange(len(X_res))\n",
    "    batch_data =   (X_BCs_U,\n",
    "                    X_BCs_L,\n",
    "                    X_BCs_R,\n",
    "                    X_BCs_Lf,\n",
    "                    X_res)\n",
    "    params, lambdas, opt_state_w, batch_losses, grad_w = update(\n",
    "    params, lambdas, opt_state_w, batch_data,idx_res,epoch)\n",
    "\n",
    "    if epoch==1 or (epoch%50)==0:\n",
    "        end_time = time.time()\n",
    "        #Get Losses\n",
    "        epoch_losses.append(batch_losses)\n",
    "        loss_avg: np.ndarray = np.mean(np.array(jax.device_get(epoch_losses)), axis=0)\n",
    "        (\n",
    "                loss,\n",
    "                loss_helmholtz,\n",
    "                loss_u_bcs,\n",
    "        ) = loss_avg\n",
    "        log_lambdas.append(jax.device_get(lambdas))     \n",
    "        # Learnig Rate\n",
    "        optim_step =  epoch\n",
    "        lr = lr0 * decay_rate ** (optim_step / decay_step)\n",
    "        ##Store\n",
    "        log_epoch.append(epoch)\n",
    "        log_loss.append(loss_avg)\n",
    "        #Get Errors\n",
    "        u_pred,e_helmholtz,errors,Errors=get_errors(epoch,Errors,num_epochs_adam,params,\n",
    "                                                    X_exact)\n",
    "        log_u_pred.append(u_pred)\n",
    "        log_res.append(e_helmholtz)\n",
    "        Error    =(\n",
    "                +loss_helmholtz\n",
    "                +loss_u_bcs)\n",
    "        lambda_bcs_print=np.mean(lamB*lambdas['BCs'])\n",
    "        lambda_res_print=np.mean(lamE*lambdas['Res'])\n",
    "\n",
    "        print(\"-\" * 50)\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Time: {end_time-start_time:.2f}s, Learning Rate: {lr:.1e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Total Error: {Error:.3e}, Total Loss: {loss:.3e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Relative L2: {errors[0,0]:.3e}, Relative Linfty: {errors[0,1]:.3e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Loss_helmholtz: {loss_helmholtz:.3e}, Loss BCs(u): {loss_u_bcs:.3e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Lambda BCs: {lambda_bcs_print:.3e}, Lambda PDE: {lambda_res_print:.3e}\")\n",
    "\n",
    "        start_time = time.time()\n",
    "\n",
    "end_time2 = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e089f02",
   "metadata": {},
   "source": [
    "# LBFGs Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27be7536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For L-BFGS-B\n",
    "def get_losses(params,X_res):\n",
    "    lamE_it=use_RBA*lambdas['Res']+(1-use_RBA)\n",
    "    lamB_it=1\n",
    "    # Call Functions\n",
    "    eq1                 = PDE(params)\n",
    "    u_fx                = u_model(params)\n",
    "    # Residuals prediction\n",
    "    e_helmholtz         = vmap(eq1, (0))(X_res)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_up          = X_BCs_U[:, :2]\n",
    "    u_pred_bcs_up  = vmap(u_fx, (0))(tx_up)[:,None]\n",
    "    # BCs prediction \n",
    "    tx_low        = X_BCs_L[:, :2]\n",
    "    u_pred_bcs_low= vmap(u_fx, (0))(tx_low)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_r        = X_BCs_R[:, :2]\n",
    "    u_pred_bcs_r= vmap(u_fx, (0))(tx_r)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_lft        = X_BCs_Lf[:, :2]\n",
    "    u_pred_bcs_lft= vmap(u_fx, (0))(tx_lft)[:,None]\n",
    "    \n",
    "    #Loss Equation\n",
    "    loss_helmholtz           = lamE*Error_fn(e_helmholtz,0.0,weight=lamE_it)\n",
    "    #Loss Bcs u\n",
    "    loss_up                  = lamB*Error_fn(u_pred_bcs_up,0.0,weight=lamB_it)\n",
    "    loss_low                 = lamB*Error_fn(u_pred_bcs_low,0.0,weight=lamB_it)\n",
    "    loss_r                   = lamB*Error_fn(u_pred_bcs_r,0.0,weight=lamB_it)\n",
    "    loss_lf                  = lamB*Error_fn(u_pred_bcs_lft,0.0,weight=lamB_it)\n",
    "    #Total Loss\n",
    "    loss                = (        \n",
    "                            + loss_helmholtz\n",
    "                            + loss_up\n",
    "                            + loss_low\n",
    "                            + loss_r\n",
    "                            + loss_lf\n",
    "                            )\n",
    "    all_losses=[\n",
    "        loss,\n",
    "        Error_fn(e_helmholtz,0.0),\n",
    "        jnp.mean(jnp.square(u_pred_bcs_up)\n",
    "                    +jnp.square(u_pred_bcs_low)\n",
    "                    +jnp.square(u_pred_bcs_up)\n",
    "                    +jnp.square(u_pred_bcs_up)),]\n",
    "    return all_losses\n",
    "\n",
    "def loss_fn(params):\n",
    "    lamE_it=use_RBA*lambdas['Res']+(1-use_RBA)\n",
    "    lamB_it=1\n",
    "    # Call Functions\n",
    "    eq1                 = PDE(params)\n",
    "    u_fx                = u_model(params)\n",
    "    # Residuals prediction\n",
    "    e_helmholtz         = vmap(eq1, (0))(X_res)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_up          = X_BCs_U[:, :2]\n",
    "    u_pred_bcs_up  = vmap(u_fx, (0))(tx_up)[:,None]\n",
    "    # BCs prediction \n",
    "    tx_low        = X_BCs_L[:, :2]\n",
    "    u_pred_bcs_low= vmap(u_fx, (0))(tx_low)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_r        = X_BCs_R[:, :2]\n",
    "    u_pred_bcs_r= vmap(u_fx, (0))(tx_r)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_lft        = X_BCs_Lf[:, :2]\n",
    "    u_pred_bcs_lft= vmap(u_fx, (0))(tx_lft)[:,None]\n",
    "    \n",
    "    #Loss Equation\n",
    "    loss_helmholtz           = lamE*Error_fn(e_helmholtz,0.0,weight=lamE_it)\n",
    "    #Loss Bcs u\n",
    "    loss_up                  = lamB*Error_fn(u_pred_bcs_up,0.0,weight=lamB_it)\n",
    "    loss_low                 = lamB*Error_fn(u_pred_bcs_low,0.0,weight=lamB_it)\n",
    "    loss_r                   = lamB*Error_fn(u_pred_bcs_r,0.0,weight=lamB_it)\n",
    "    loss_lf                  = lamB*Error_fn(u_pred_bcs_lft,0.0,weight=lamB_it)\n",
    "    #Total Loss\n",
    "    loss                = (        \n",
    "                            + loss_helmholtz\n",
    "                            + loss_up\n",
    "                            + loss_low\n",
    "                            + loss_r\n",
    "                            + loss_lf\n",
    "                            )\n",
    "    return loss\n",
    "            \n",
    "def Callback_Loss(params_i):\n",
    "    global log_loss,log_lambdas\n",
    "    global log_epoch\n",
    "    global epoch\n",
    "    global start_time\n",
    "    global Errors\n",
    "    global Mu\n",
    "    global Sigmas\n",
    "    global lambdas,gamma,lr_lambdas_0\n",
    "    if  epoch%1==0:\n",
    "        if  epoch%100==0:\n",
    "            end_time = time.time()\n",
    "            all_losses=get_losses(params_i,X_res)\n",
    "            loss           =all_losses[0]\n",
    "            loss_helmholtz =all_losses[1]\n",
    "            loss_u_bcs     =all_losses[2]\n",
    "            # Learnig Rate\n",
    "            optim_step =  epoch\n",
    "            ##Store\n",
    "            log_epoch.append(epoch)\n",
    "            #Plot Lambdas\n",
    "            u_pred,e_helmholtz,errors,Errors=get_errors(epoch,Errors,\n",
    "                                                        num_epochs_adam+num_epochs_lbfgsb,\n",
    "                                                        params_i,\n",
    "                                                        X_exact,)\n",
    "            log_lambdas.append(lambdas)\n",
    "            log_u_pred.append(u_pred)\n",
    "            log_res.append(e_helmholtz)\n",
    "            Error    =(\n",
    "                    +loss_helmholtz\n",
    "                    +loss_u_bcs)\n",
    "            lambda_bcs_print=np.mean(lamB*lambdas['BCs'])\n",
    "            lambda_res_print=np.mean(lamE*lambdas['Res'])\n",
    "            print(\"-\" * 50)\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Time: {end_time-start_time:.2f}s, Learning Rate: {lr:.1e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Total Error: {Error:.3e}, Total Loss: {loss:.3e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Relative L2: {errors[0,0]:.3e}, Relative Linfty: {errors[0,1]:.3e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Loss_helmholtz: {loss_helmholtz:.3e}, Loss BCs(u): {loss_u_bcs:.3e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Lambda BCs: {lambda_bcs_print:.3e}, Lambda PDE: {lambda_res_print:.3e}\")\n",
    "\n",
    "            start_time = time.time()\n",
    "    epoch += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87e513f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Epoch: 20000, It: 20000, Time: 13.59s, Learning Rate: 4.0e-06\n",
      "Epoch: 20000, It: 20000, Total Error: 4.889e-04, Total Loss: 7.051e-04\n",
      "Epoch: 20000, It: 20000, Relative L2: 1.025e-04, Relative Linfty: 1.569e-04\n",
      "Epoch: 20000, It: 20000, Loss_helmholtz: 4.889e-04, Loss BCs(u): 3.684e-09\n",
      "Epoch: 20000, It: 20000, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20100, It: 20100, Time: 18.35s, Learning Rate: 4.0e-06\n",
      "Epoch: 20100, It: 20100, Total Error: 2.007e-04, Total Loss: 3.130e-04\n",
      "Epoch: 20100, It: 20100, Relative L2: 3.557e-05, Relative Linfty: 4.879e-05\n",
      "Epoch: 20100, It: 20100, Loss_helmholtz: 2.007e-04, Loss BCs(u): 2.005e-10\n",
      "Epoch: 20100, It: 20100, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20200, It: 20200, Time: 18.26s, Learning Rate: 4.0e-06\n",
      "Epoch: 20200, It: 20200, Total Error: 1.921e-04, Total Loss: 3.012e-04\n",
      "Epoch: 20200, It: 20200, Relative L2: 9.460e-06, Relative Linfty: 1.623e-05\n",
      "Epoch: 20200, It: 20200, Loss_helmholtz: 1.921e-04, Loss BCs(u): 4.676e-11\n",
      "Epoch: 20200, It: 20200, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "Step 2:\n",
      "--------------------------------------------------\n",
      "Epoch: 20300, It: 20300, Time: 18.80s, Learning Rate: 4.0e-06\n",
      "Epoch: 20300, It: 20300, Total Error: 1.892e-04, Total Loss: 2.972e-04\n",
      "Epoch: 20300, It: 20300, Relative L2: 6.556e-06, Relative Linfty: 1.132e-05\n",
      "Epoch: 20300, It: 20300, Loss_helmholtz: 1.892e-04, Loss BCs(u): 2.186e-11\n",
      "Epoch: 20300, It: 20300, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20400, It: 20400, Time: 18.31s, Learning Rate: 4.0e-06\n",
      "Epoch: 20400, It: 20400, Total Error: 1.849e-04, Total Loss: 2.912e-04\n",
      "Epoch: 20400, It: 20400, Relative L2: 1.544e-05, Relative Linfty: 2.783e-05\n",
      "Epoch: 20400, It: 20400, Loss_helmholtz: 1.849e-04, Loss BCs(u): 1.926e-11\n",
      "Epoch: 20400, It: 20400, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "Step 3:\n",
      "--------------------------------------------------\n",
      "Epoch: 20500, It: 20500, Time: 18.75s, Learning Rate: 4.0e-06\n",
      "Epoch: 20500, It: 20500, Total Error: 1.832e-04, Total Loss: 2.887e-04\n",
      "Epoch: 20500, It: 20500, Relative L2: 9.089e-06, Relative Linfty: 1.572e-05\n",
      "Epoch: 20500, It: 20500, Loss_helmholtz: 1.832e-04, Loss BCs(u): 1.875e-11\n",
      "Epoch: 20500, It: 20500, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20600, It: 20600, Time: 18.00s, Learning Rate: 4.0e-06\n",
      "Epoch: 20600, It: 20600, Total Error: 1.820e-04, Total Loss: 2.870e-04\n",
      "Epoch: 20600, It: 20600, Relative L2: 5.549e-06, Relative Linfty: 1.156e-05\n",
      "Epoch: 20600, It: 20600, Loss_helmholtz: 1.820e-04, Loss BCs(u): 3.858e-11\n",
      "Epoch: 20600, It: 20600, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "Step 4:\n",
      "--------------------------------------------------\n",
      "Epoch: 20700, It: 20700, Time: 18.40s, Learning Rate: 4.0e-06\n",
      "Epoch: 20700, It: 20700, Total Error: 1.812e-04, Total Loss: 2.860e-04\n",
      "Epoch: 20700, It: 20700, Relative L2: 4.754e-06, Relative Linfty: 1.171e-05\n",
      "Epoch: 20700, It: 20700, Loss_helmholtz: 1.812e-04, Loss BCs(u): 1.843e-11\n",
      "Epoch: 20700, It: 20700, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20800, It: 20800, Time: 17.95s, Learning Rate: 4.0e-06\n",
      "Epoch: 20800, It: 20800, Total Error: 1.799e-04, Total Loss: 2.840e-04\n",
      "Epoch: 20800, It: 20800, Relative L2: 8.136e-06, Relative Linfty: 1.743e-05\n",
      "Epoch: 20800, It: 20800, Loss_helmholtz: 1.799e-04, Loss BCs(u): 1.408e-11\n",
      "Epoch: 20800, It: 20800, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "Step 5:\n",
      "--------------------------------------------------\n",
      "Epoch: 20900, It: 20900, Time: 18.37s, Learning Rate: 4.0e-06\n",
      "Epoch: 20900, It: 20900, Total Error: 1.787e-04, Total Loss: 2.824e-04\n",
      "Epoch: 20900, It: 20900, Relative L2: 6.270e-06, Relative Linfty: 1.285e-05\n",
      "Epoch: 20900, It: 20900, Loss_helmholtz: 1.787e-04, Loss BCs(u): 1.587e-11\n",
      "Epoch: 20900, It: 20900, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 21000, It: 21000, Time: 18.00s, Learning Rate: 4.0e-06\n",
      "Epoch: 21000, It: 21000, Total Error: 1.783e-04, Total Loss: 2.817e-04\n",
      "Epoch: 21000, It: 21000, Relative L2: 5.837e-06, Relative Linfty: 1.164e-05\n",
      "Epoch: 21000, It: 21000, Loss_helmholtz: 1.783e-04, Loss BCs(u): 4.764e-11\n",
      "Epoch: 21000, It: 21000, Lambda BCs: 6.377e+01, Lambda PDE: 1.359e+00\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "optimizer2=jaxopt.ScipyMinimize(method='L-BFGS-B',\n",
    "                               fun=loss_fn,\n",
    "                               callback=Callback_Loss,\n",
    "                               maxiter=int(num_epochs_lbfgsb/substepts_lbfgs)+1,\n",
    "                               jit=True,\n",
    "                               tol= 1e-99,\n",
    "                               dtype=np.float64)\n",
    "if num_epochs_lbfgsb>0:\n",
    "    config.update(\"jax_enable_x64\", True)\n",
    "    for i in range(substepts_lbfgs):\n",
    "        print(f'Step {i+1}:')\n",
    "        params,opt_state=optimizer2.run(params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4cbda87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final RL2 error: 5.829e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u_fx                = u_model(params)\n",
    "#prediction \n",
    "xy, u_real          = X_exact[:, :2], X_exact[:, 2:]\n",
    "u_pred              = vmap(u_fx, (0))(xy)[:,None]\n",
    "u_pred              =jax.device_get(u_pred)\n",
    "a=relative_error2(u_pred.flatten(),u_real.flatten())\n",
    "print(f'Final RL2 error: {a:.3e}')\n",
    "U_pred=np.reshape(u_pred,(nx,ny))\n",
    "Error=np.abs(u-U_pred)\n",
    "Exact=np.array([u,U_pred,Error])  \n",
    "plot3D_mat(X,Y,Exact,f_names=['Actual u(x,y)','Predicted u(x,y)',f'Relative L2={a:.3e}'],cmap='RdBu_r',window=5,font_size=14)\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3681a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 412/412 [00:00<00:00, 1980.37it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_res_min=[]\n",
    "lambda_res_max=[]\n",
    "lambda_res_mean=[]\n",
    "mpl.rcParams['font.size'] = 16\n",
    "for i in tqdm(range(len(log_lambdas))):\n",
    "    lambda_res_min.append(lamE*np.min(log_lambdas[i]['Res']))\n",
    "    lambda_res_max.append(lamE*np.max(log_lambdas[i]['Res']))\n",
    "    lambda_res_mean.append(lamE*np.mean(log_lambdas[i]['Res']))\n",
    "fig, axs = plt.subplots(1,1,figsize=(4,4))\n",
    "axs.plot(np.arange(len(lambda_res_max))*50, (lambda_res_min), label = f'Min Lambda')\n",
    "axs.plot(np.arange(len(lambda_res_max))*50, (lambda_res_max), label = f'Max Lambda')\n",
    "axs.plot(np.arange(len(lambda_res_mean))*50,(lambda_res_mean), label = f'Mean Lambda')\n",
    "axs.set_xlabel('Epoch')\n",
    "axs.legend(loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff76ad89",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "Anagnostopoulos, S. J., Toscano, J. D., Stergiopulos, N., & Karniadakis, G. E. (2024). Residual-based attention in physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering, 421, 116805.\n",
    "\n",
    "Link: https://www.sciencedirect.com/science/article/pii/S0045782524000616?dgcid=coauthor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffce977",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  },
  "vscode": {
   "interpreter": {
    "hash": "f309334e5f4406e5b2ce0c18f807d883176a73f7f1a0cd119c91872201c4e9da"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
